CFPB: Consumer Dispute? classification:: CFPB-PrepaidCard-Rest-02
bdanalytics
Date: (Thu) Oct 20, 2016
Introduction:
Data: Source: Training: http://catalog.data.gov/dataset/consumer-complaint-database
New: None
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Background
US Federal Government: Bureau of Consumer Financial Protection (CFPB): Consumer Complaint Database: Metadata Updated: Sep 26, 2015
CFPB can increase its effectiveness & customer satisfaction by minimizing disputes escalated by consumers when businesses initially respond to conusmer complaints in a timely manner.
The objective is to create a classifier that predicts if a specific customer will dispute a complaint with a bank: Consumer Dispute? (Yes or No).
The observations are complaints CFPB received about financial products and services from US residents.
Results
Display ggtile plot of Test data frame observations
Display ggtile plot of Validation data frame observations
Display AUC Curve
Display classifier threshold selection plot
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Summary of key steps & error improvement stats:
Potential next steps include:
- Feature Engineering:
- DateRecieved, DateSenttocompany:
- Add Federal, State & City holidays for each year
- Add number of complaints received and sent for this complaint’s date
Product: Include all & create product groups to minimize model stacks
- NLP: Issue, SubIssue, “Consumercomplaintnarrative”, “Companypublicresponse”
- Company:
- Identify Industry based on stock / Dun & Bradstreet listing
- Create clusters based on Product, Subproduct & Company
- Test options
- Preprocessing (center, scaling, PCA, etc.)
- Resampling (Boot strapping, LGOCV, etc.)
Ensemble models
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
debugSource("~/Dropbox/datascience/R/mydsutils.R") else
source("~/Dropbox/datascience/R/mydsutils.R") ## Loading required package: proxy##
## Attaching package: 'proxy'## The following objects are masked from 'package:stats':
##
## as.dist, dist## The following object is masked from 'package:base':
##
## as.matrix## Loading required package: caret## Loading required package: lattice# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("complete", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
#glbObsTrnFile <- list(url = "http://catalog.data.gov/dataset/consumer-complaint-database"
glbObsTrnFile <- list(name = "Consumer_Complaints.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
, splitSpecs = list(method = "condition" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
,condition = 'Consumerdisputed == ""'# or 'is.na(<var>)'; '<var> <condition_operator> <value>'
)
)
glbObsNewFile <- NULL # default OR list(url = "None")
glbObsDropCondition <- #NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# '(glbObsAll[, "Product"] %in% c("Mortgage", "Debt collection", "Credit reporting", "Credit card", "Bank account or service", NULL, "Payday loan", "Money transfers", "Prepaid card", "Other financial service", "Virtual currency")) '
'(!(glbObsAll[, "Product"] %in% c("Prepaid card")))'
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Consumerdisputed"
# for classification, the response variable has to be a factor
glb_rsp_var <- "CDisputed.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
#
# chk ref value against frequencies vs. alpha sort order
ret_vals <- rep_len(NA, length(raw));
ret_vals[raw != ""] <- ifelse(raw[raw != ""] == "Yes", "Y", "N");
return(relevel(as.factor(ret_vals), ref="N"))
#
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
#print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
levels(var)[as.numeric(var)]
# sapply(levels(var)[as.numeric(var)], function(elm)
# if (is.na(elm)) return(elm) else
# if (elm == 'R') return("Republican") else
# if (elm == 'D') return("Democrat") else
# stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
# )
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
#print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# "Datereceived":
# "Product":
# "Subproduct":
# "Issue":
# "Subissue":
# "Consumercomplaintnarrative":
# "Companypublicresponse":
# "Company":
# "State":
# "ZIPcode":
# "Tags":
# "Consumerconsentprovided":
# "Submittedvia":
# "Datesenttocompany":
# "Companyresponsetoconsumer":
# "Timelyresponse":
# "Consumerdisputed": "", "No", "Yes"; glb_rsp_var_raw
# "ComplaintID": glbFeatsId
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "ComplaintID" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- "gSPrd.fctr" #NULL # choose from c(NULL : default, "<category_feat>")
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "Datereceived"
, "Product"
, "Subproduct"
, "Issue"
, "Subissue"
, "Consumercomplaintnarrative"
, "Companypublicresponse"
, "Company"
, "State"
, "ZIPcode"
, "Tags"
, "Consumerconsentprovided"
, "Submittedvia"
, "Datesenttocompany"
, "Companyresponsetoconsumer"
#, "Timelyresponse"
)
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
glbFeatsDerive[["Sent.Recd.Dys"]] <- list(
mapfn = function(raw1, raw2) { return(as.numeric(difftime(strptime(raw1, format = "%m/%d/%Y"), strptime(raw2, format = "%m/%d/%Y"), units = "days"))) }
, args = c("Datesenttocompany", "Datereceived"))
#smp <- mapfn(head(glbObsAll$Datesenttocompany), head(glbObsAll$Datereceived))
glbFeatsDerive[["gSPrd.fctr"]] <- list(
mapfn = function(raw1) { raw <- gsub(" ", "", raw1);
retVal <- dplyr::recode_factor(raw,
"ElectronicBenefitTransfer/EBTcard" = "Other",
"Transitcard" = "Other",
"Otherspecialpurposecard" = "Other"
)
return(factor(retVal))
}
, args = c("Subproduct"))
#smp <- mapfn(subset(glbObsAll, Product %in% "Prepaid card")$Subproduct); print(table(smp, useNA = "ifany"))
glbFeatsDerive[["gCompany"]] <- list(
mapfn = function(raw1) {
# Freq 1
raw <- gsub("(Woods Oviatt Gilman LLP|Washington Federal|Umpqua Holdings Corporation|USAA Savings|UMB Bank|TCF National Bank|Pay-O-Matic|New York Community Bank|Moneytree Inc|Goldman Sachs Bank USA|First Citizens|Equifax|Enova International, Inc\\.|ERC|Circle Internet Financial|Check into Cash, Inc\\.|CashCall, Inc\\.|Barclays PLC|Banco Popular North America|BMO Harris|Atlanticus Services Corporation|Arvest Bank)", "Other", raw1);
# Freq 2
raw <- gsub("(Pentagon FCU|MB Financial, INC|Fidelity National Information Services, Inc\\. \\(FNIS\\)|Encore Capital Group|Discover|BBVA Compass)", "Other", raw);
# raw <- gsub("(Discover|BBVACompass)", "Other", raw);
# Freq 3
raw <- gsub("(Travelex NY, Inc\\.|Plastyc, Inc\\.|HSBC North America Holdings Inc\\.|Continental Finance Company, LLC|BB&T Financial|Automatic Data Processing, Inc\\.|ACE Cash Express Inc\\.)", "Other", raw);
# Freq 4-9
raw <- gsub("(MoneyGram|KeyBank NA|Citizens Financial Group, Inc\\.|PLS GROUP, INC|CARD Corporation|SunTrust Banks, Inc\\.|Regions Financial Corporation|Synovus Bank|Fifth Third Financial Corporation)", "Other", raw);
# Freq >= 10 < 20
raw <- gsub("(The Western Union Company|TD Bank US Holding Company|PNC Bank N\\.A\\.|First Tennessee Bank|Synchrony Financial|JPay Inc\\.|Capital One)", "Other", raw);
# Freq >= 20 < 100
raw <- gsub("(Google Inc\\.|Wells Fargo & Company|First Data Corporation|JPMorgan Chase & Co\\.|Incomm Holdings, Inc\\.|Bank of America)", "Other", raw);
return(gsub(" ", "", raw))
#return(raw)
}
, args = c("Company"))
#smp <- mapfn(subset(glbObsAll, Product %in% "Prepaid card")$Company); print(sort(table(smp, useNA = "ifany"), decreasing = TRUE))
glbFeatsDerive[["Rgn.Dvn"]] <- list(
mapfn = function(raw1) { raw <- gsub(" ", "XX", raw1, fixed = TRUE);
raw <- gsub("(CT|ME|MA|NH|RI|VT)", "NE#NewEngland", raw);
raw <- gsub("(NJ|NY|PA)", "NE#MidAtlantic", raw);
raw <- gsub("(IL|IN|MI|OH|WI)", "MW#EastNorthCentral", raw);
raw <- gsub("(IA|KS|MN|MO|NE|ND|SD)", "MW#WestNorthCentral", raw);
raw <- gsub("(DE|FL|GA|MD|NC|SC|VA|DC|WV)", "SH#SouthAtlantic", raw);
raw <- gsub("(AL|KY|MS|TN)", "SH#EastSouthCentral", raw);
raw <- gsub("(AR|LA|OK|TX)", "SH#WestSouthCentral", raw);
raw <- gsub("(AZ|CO|ID|MT|NV|NM|UT|WY)", "WT#Mountain", raw);
raw <- gsub("(AK|CA|HI|OR|WA)", "WT#Pacific", raw);
raw[!grepl("#", raw)] <- "OT#Other";
return(raw) }
, args = c("State"))
#smp <- mapfn(glbObsAll$State); print(sort(table(smp, useNA = "ifany")))
glbFeatsDerive[["gTags"]] <- list(
mapfn = function(raw1) { raw <- gsub("( |,)", "", raw1);
raw[raw == ""] <- "Other";
return(raw) }
, args = c("Tags"))
#smp <- mapfn(glbObsAll$Tags); print(sort(table(smp, useNA = "ifany")))
glbFeatsDerive[["gCConsent"]] <- list(
mapfn = function(raw1) { raw <- gsub("( |/)", "", raw1);
raw[raw == ""] <- "NA";
return(raw) }
, args = c("Consumerconsentprovided"))
#smp <- mapfn(glbObsAll$Consumerconsentprovided); print(sort(table(smp, useNA = "ifany")))
glbFeatsDerive[["Channel"]] <- list(
mapfn = function(raw1) { raw <- gsub(" ", "", raw1, fixed = TRUE);
return(raw) }
, args = c("Submittedvia"))
#smp <- mapfn(glbObsAll$Submittedvia); print(sort(table(smp, useNA = "ifany")))
glbFeatsDerive[["Response"]] <- list(
mapfn = function(raw1) { raw <- gsub(" ", "", raw1, fixed = TRUE);
return(raw) }
, args = c("Companyresponsetoconsumer"))
#smp <- mapfn(glbObsAll$Companyresponsetoconsumer); print(sort(table(smp, useNA = "ifany")))
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(raw1) { return(1:length(raw1)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(raw1) { return(1:length(raw1)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(Sent.Recd.Dys) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
glbFeatsDerive[["Sent.Recd.Dys.log1p"]] <- list(
mapfn = function(raw1) { return(log1p(raw1 + 2)) } # Account for < -1
, args = c("Sent.Recd.Dys"))
glbFeatsDerive[["Sent.Recd.Dys.root2"]] <- list(
mapfn = function(raw1) { return((raw1 + 2) ^ (1/2)) }
, args = c("Sent.Recd.Dys"))
glbFeatsDerive[["Sent.Recd.Dys.nexp"]] <- list(
mapfn = function(raw1) { return(exp(-raw1)) }
, args = c("Sent.Recd.Dys"))
#print(summary(glbObsAll$Sent.Recd.Dys)); print(summary(mapfn(glbObsAll$Sent.Recd.Dys)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# mapfn = function(raw1) { raw <- 2016 - raw1;
# # raw[!is.na(raw) & raw >= 2010] <- NA
# raw[!is.na(raw) & (raw <= 15)] <- NA
# raw[!is.na(raw) & (raw >= 90)] <- NA
# retVal <- rep_len("NA", length(raw))
# # breaks = c(1879, seq(1949, 1989, 10), 2049)
# # cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# # labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
# cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
# retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
# return(factor(retVal, levels = c("NA"
# ,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
# ordered = TRUE))
# }
# , args = c("District"))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# for (qsn in grep("Q12", glbFeatsExclude, fixed = TRUE, value = TRUE))
# glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
# mapfn = function(raw1) {
# raw1[raw1 %in% ""] <- "NA"
# rawVal <- unique(raw1)
#
# if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
# raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
# if (length(setdiff(rawVal, expVal)) > 0)
# stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
# " does not match expectation: ", paste0(expVal, collapse = "|"))
# } else
# if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
# raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
# raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)
# if (length(setdiff(rawVal, expVal)) > 0)
# stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
# " does not match expectation: ", paste0(expVal, collapse = "|"))
# }
#
# return(relevel(as.factor(raw1), ref = "NA"))
# }
# , args = c(qsn))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsDateTime[["Datereceived"]] <-
c(format = "%m/%d/%Y", impute.na = TRUE, last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsDateTime[["Datesenttocompany"]] <-
c(format = "%m/%d/%Y", impute.na = TRUE, last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <-
sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glbFeatsCluster <- '%<d-% setdiff(names(glbObsAll), c(glb_rsp_var_raw, glb_rsp_var, glbFeatsExclude, glbFeatsCategory, glbFeatsId, ".rnorm", ".pos"))' # NULL: default or c("<feat1>", "<feat2>") or '%<d-% <expr>'
glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- glb_rsp_var #NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbRFEResults <- NULL
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
# RFE = "Recursive Feature Elimination"
# Csm = CuStoM
# NOr = No OutlieRs
# Inc = INteraCt
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet")
} else {
glbMdlFamilies[["All.X"]] <- glbMdlMethods # c("glmnet", "glm")
}
#glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlSequential <- c(NULL
#, "All.X#zv.pca#rcv#glmnet"
)
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
# AllX__rcv_glmnetTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(parameter = "lambda", vals = "9.342e-02")
# ) # max.Accuracy.OOB = 0.5956175 @ 0.325 0.03345007
# glbMdlTuneParams <- rbind(glbMdlTuneParams
# ,cbind(data.frame(mdlId = "All.X##rcv#glmnet"), AllX__rcv_glmnetTuneParams)
# ,cbind(data.frame(mdlId = "Final.All.X##rcv#glmnet"), FinalAllX__rcv_glmnetTuneParams)
# )
Low_cor_X__rcv_glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "2.658826e-04 1.234118e-03 5.728268e-03 2.658826e-02 0.05317652")
) # max.Accuracy.OOB = 0.5956175 @ 0.325 0.03345007
glbMdlTuneParams <- rbind(glbMdlTuneParams
,cbind(data.frame(mdlId = "Low.cor.X##rcv#glmnet"), Low_cor_X__rcv_glmnetTuneParams)
# ,cbind(data.frame(mdlId = "Final.All.X##rcv#glmnet"), FinalAllX__rcv_glmnetTuneParams)
)
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# bagEarthTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "degree", vals = "1")
# ,data.frame(parameter = "nprune", vals = "256")
# )
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
pkgPreprocMethods <-
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
# Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
c(NULL
,"zv", "nzv"
,"BoxCox", "YeoJohnson", "expoTrans"
,"center", "scale", "center.scale", "range"
,"knnImpute", "bagImpute", "medianImpute"
,"zv.pca", "ica", "spatialSign"
,"conditionalX")
glbMdlPreprocMethods <- list(NULL # default
# ,"All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# # c(NULL)))
# c("zv.YeoJohnson.pca")))
# ,"RFE.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# c(NULL)))
# # c("zv.pca.spatialSign")))
)
# glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
# "nzv.pca.spatialSign"))
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 7 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "min.elapsedtime.everything",
"max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB",
"min.elapsedtime.everything",
# "min.aic.fit",
"max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.elapsedtime.everything")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glbMdlEnsemble <- NULL # default # options: "auto"
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
glbMdlEnsembleSampleMethods <- c("boot", "boot632", "cv", "repeatedcv"
# , "LOOCV" # tuneLength * nrow(fitDF) # way too many models
, "LGOCV"
, "adaptive_cv"
, "adaptive_boot"
, "adaptive_LGOCV"
)
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSltId <- NULL #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFnlId <- NULL #select from c(NULL, glbMdlSltId)
glbMdlFnlNslId <- NULL
glbMdlFnlRslId <- NULL
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutDf) {
# require(tidyr)
# obsOutDf <- obsOutDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutDf) {
# }
)
#obsOutFnlNslDf <- savobsOutFnlNslDf
# glbObsOut$mapFn <- function(obsOutDf) {
# txfout_df <- dplyr::select(obsOutDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFnlNslDf <- glbObsOut$mapFn(obsOutFnlNslDf); print(head(obsOutFnlNslDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlFnlNslId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFnlNslId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
glbOutStackFnames <- NULL #: default
# c("ebayipads_txt_assoc1_out_bid1_stack.csv")
glbOut <- list(pfx = "CFPB-PrepaidCard-Rest-02_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- "select.features" #default: script will load envir from previous chunk
glbChunks[["last"]] <- NULL #default: script will save envir at end of this chunk
glbChunks[["inpFilePathName"]] <- "data/CFPB-PrepaidCard-partition-data-training-01_partition.data.training.RData" #mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Trn..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL,
ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))## label step_major step_minor label_minor bgn end elapsed
## 1 select.features 1 0 0 4.371 NA NAStep 1.0: select features
chunk option: eval=
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
Step 1.0: select features
## Warning in as.POSIXlt.POSIXct(x, tz): unknown timezone 'NA'
## Warning in as.POSIXlt.POSIXct(x, tz): unknown timezone 'NA'## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## Loading required package: reshape2## Warning in as.POSIXlt.POSIXct(x, tz): unknown timezone 'NA'## Warning in as.POSIXlt.POSIXct(x, tz): unknown timezone 'NA'
## Warning in as.POSIXlt.POSIXct(x, tz): unknown timezone 'NA'
## Warning in as.POSIXlt.POSIXct(x, tz): unknown timezone 'NA'
## Warning in as.POSIXlt.POSIXct(x, tz): unknown timezone 'NA'
## Warning in as.POSIXlt.POSIXct(x, tz): unknown timezone 'NA'
## Warning in as.POSIXlt.POSIXct(x, tz): unknown timezone 'NA'
## Warning in as.POSIXlt.POSIXct(x, tz): unknown timezone 'NA'## [1] "cor(Datereceived.date.fctr, Datesenttocompany.date.fctr)=1.0000"
## [1] "cor(CDisputed.fctr, Datereceived.date.fctr)=-0.0679"
## [1] "cor(CDisputed.fctr, Datesenttocompany.date.fctr)=-0.0679"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datesenttocompany.date.fctr as highly correlated
## with Datereceived.date.fctr## [1] "cor(Datereceived.hlday, Datesenttocompany.hlday)=1.0000"
## [1] "cor(CDisputed.fctr, Datereceived.hlday)=0.0136"
## [1] "cor(CDisputed.fctr, Datesenttocompany.hlday)=0.0136"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datesenttocompany.hlday as highly correlated with
## Datereceived.hlday## [1] "cor(Datereceived.juliandate, Datesenttocompany.juliandate)=1.0000"
## [1] "cor(CDisputed.fctr, Datereceived.juliandate)=-0.0677"
## [1] "cor(CDisputed.fctr, Datesenttocompany.juliandate)=-0.0677"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datesenttocompany.juliandate as highly correlated
## with Datereceived.juliandate## [1] "cor(Datereceived.last16.log1p, Datesenttocompany.last16.log1p)=1.0000"
## [1] "cor(CDisputed.fctr, Datereceived.last16.log1p)=0.1509"
## [1] "cor(CDisputed.fctr, Datesenttocompany.last16.log1p)=0.1509"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datesenttocompany.last16.log1p as highly correlated
## with Datereceived.last16.log1p## [1] "cor(Datereceived.last2.log1p, Datesenttocompany.last2.log1p)=1.0000"
## [1] "cor(CDisputed.fctr, Datereceived.last2.log1p)=0.1598"
## [1] "cor(CDisputed.fctr, Datesenttocompany.last2.log1p)=0.1598"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datesenttocompany.last2.log1p as highly correlated
## with Datereceived.last2.log1p## [1] "cor(Datereceived.last32.log1p, Datesenttocompany.last32.log1p)=1.0000"
## [1] "cor(CDisputed.fctr, Datereceived.last32.log1p)=0.1220"
## [1] "cor(CDisputed.fctr, Datesenttocompany.last32.log1p)=0.1220"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datesenttocompany.last32.log1p as highly correlated
## with Datereceived.last32.log1p## [1] "cor(Datereceived.last4.log1p, Datesenttocompany.last4.log1p)=1.0000"
## [1] "cor(CDisputed.fctr, Datereceived.last4.log1p)=0.1623"
## [1] "cor(CDisputed.fctr, Datesenttocompany.last4.log1p)=0.1623"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datesenttocompany.last4.log1p as highly correlated
## with Datereceived.last4.log1p## [1] "cor(Datereceived.last8.log1p, Datesenttocompany.last8.log1p)=1.0000"
## [1] "cor(CDisputed.fctr, Datereceived.last8.log1p)=0.1642"
## [1] "cor(CDisputed.fctr, Datesenttocompany.last8.log1p)=0.1642"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datesenttocompany.last8.log1p as highly correlated
## with Datereceived.last8.log1p## [1] "cor(Datereceived.month.fctr, Datesenttocompany.month.fctr)=1.0000"
## [1] "cor(CDisputed.fctr, Datereceived.month.fctr)=-0.0632"
## [1] "cor(CDisputed.fctr, Datesenttocompany.month.fctr)=-0.0632"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datesenttocompany.month.fctr as highly correlated
## with Datereceived.month.fctr## [1] "cor(Datereceived.wkend, Datesenttocompany.wkend)=1.0000"
## [1] "cor(CDisputed.fctr, Datereceived.wkend)=-0.0238"
## [1] "cor(CDisputed.fctr, Datesenttocompany.wkend)=-0.0238"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datesenttocompany.wkend as highly correlated with
## Datereceived.wkend## [1] "cor(Datereceived.year.fctr, Datesenttocompany.year.fctr)=1.0000"
## [1] "cor(CDisputed.fctr, Datereceived.year.fctr)=0.0497"
## [1] "cor(CDisputed.fctr, Datesenttocompany.year.fctr)=0.0497"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datesenttocompany.year.fctr as highly correlated
## with Datereceived.year.fctr## [1] "cor(Datereceived.juliandate, Datereceived.month.fctr)=0.9966"
## [1] "cor(CDisputed.fctr, Datereceived.juliandate)=-0.0677"
## [1] "cor(CDisputed.fctr, Datereceived.month.fctr)=-0.0632"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datereceived.month.fctr as highly correlated with
## Datereceived.juliandate## [1] "cor(Sent.Recd.Dys.log1p, Sent.Recd.Dys.root2)=0.9867"
## [1] "cor(CDisputed.fctr, Sent.Recd.Dys.log1p)=-0.1419"
## [1] "cor(CDisputed.fctr, Sent.Recd.Dys.root2)=-0.1412"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Sent.Recd.Dys.root2 as highly correlated with
## Sent.Recd.Dys.log1p## [1] "cor(Sent.Recd.Dys, Sent.Recd.Dys.log1p)=0.9228"
## [1] "cor(CDisputed.fctr, Sent.Recd.Dys)=-0.1299"
## [1] "cor(CDisputed.fctr, Sent.Recd.Dys.log1p)=-0.1419"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Sent.Recd.Dys as highly correlated with
## Sent.Recd.Dys.log1p## [1] "cor(ComplaintID, Datereceived.year.fctr)=0.8747"
## [1] "cor(CDisputed.fctr, ComplaintID)=0.0206"
## [1] "cor(CDisputed.fctr, Datereceived.year.fctr)=0.0497"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df
## = glbObsTrn, : Identified ComplaintID as highly correlated with
## Datereceived.year.fctr## [1] "cor(Datereceived.last16.log1p, Datereceived.last8.log1p)=0.8463"
## [1] "cor(CDisputed.fctr, Datereceived.last16.log1p)=0.1509"
## [1] "cor(CDisputed.fctr, Datereceived.last8.log1p)=0.1642"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datereceived.last16.log1p as highly correlated with
## Datereceived.last8.log1p## [1] "cor(Datereceived.last4.log1p, Datereceived.last8.log1p)=0.7265"
## [1] "cor(CDisputed.fctr, Datereceived.last4.log1p)=0.1623"
## [1] "cor(CDisputed.fctr, Datereceived.last8.log1p)=0.1642"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Datereceived.last4.log1p as highly correlated with
## Datereceived.last8.log1p## cor.y exclude.as.feat cor.y.abs
## Datereceived.last8.log1p 0.164168356 0 0.164168356
## Datesenttocompany.last8.log1p 0.164168356 0 0.164168356
## Datereceived.last4.log1p 0.162314275 0 0.162314275
## Datesenttocompany.last4.log1p 0.162314275 0 0.162314275
## Datereceived.last2.log1p 0.159849462 0 0.159849462
## Datesenttocompany.last2.log1p 0.159849462 0 0.159849462
## Datereceived.last16.log1p 0.150892831 0 0.150892831
## Datesenttocompany.last16.log1p 0.150892831 0 0.150892831
## Datereceived.last32.log1p 0.122022721 0 0.122022721
## Datesenttocompany.last32.log1p 0.122022721 0 0.122022721
## .pos 0.069085650 0 0.069085650
## gCompany.fctr 0.062837704 0 0.062837704
## gTags.fctr 0.061416859 0 0.061416859
## Sent.Recd.Dys.nexp 0.059029644 0 0.059029644
## Datereceived.zoo 0.054643150 1 0.054643150
## Datesenttocompany.zoo 0.054643150 1 0.054643150
## Timelyresponse.fctr 0.054340991 0 0.054340991
## Rgn.Dvn.fctr 0.053302356 0 0.053302356
## Datereceived.year.fctr 0.049749554 0 0.049749554
## Datesenttocompany.year.fctr 0.049749554 0 0.049749554
## ComplaintID 0.020647248 0 0.020647248
## Datereceived.POSIX 0.020575223 1 0.020575223
## Datesenttocompany.POSIX 0.020575223 1 0.020575223
## Datereceived.hlday 0.013640853 0 0.013640853
## Datesenttocompany.hlday 0.013640853 0 0.013640853
## Datereceived.wkday.fctr 0.004909536 0 0.004909536
## Datesenttocompany.wkday.fctr 0.004909536 0 0.004909536
## gCConsent.fctr -0.002776913 0 0.002776913
## .rnorm -0.004943641 0 0.004943641
## Datereceived.wkend -0.023776697 0 0.023776697
## Datesenttocompany.wkend -0.023776697 0 0.023776697
## Channel.fctr -0.031163962 0 0.031163962
## gSPrd.fctr -0.036262379 0 0.036262379
## .clusterid -0.055429506 1 0.055429506
## .clusterid.fctr -0.055429506 0 0.055429506
## Datereceived.month.fctr -0.063231079 0 0.063231079
## Datesenttocompany.month.fctr -0.063231079 0 0.063231079
## Datereceived.juliandate -0.067679757 0 0.067679757
## Datesenttocompany.juliandate -0.067679757 0 0.067679757
## Datereceived.date.fctr -0.067929494 0 0.067929494
## Datesenttocompany.date.fctr -0.067929494 0 0.067929494
## Sent.Recd.Dys -0.129861524 0 0.129861524
## Sent.Recd.Dys.root2 -0.141226398 0 0.141226398
## Sent.Recd.Dys.log1p -0.141943231 0 0.141943231
## Response.fctr -0.220910417 0 0.220910417
## Datereceived.day.minutes NA 1 NA
## Datereceived.hour.fctr NA 0 NA
## Datereceived.minute.fctr NA 0 NA
## Datereceived.second.fctr NA 0 NA
## Datesenttocompany.day.minutes NA 1 NA
## Datesenttocompany.hour.fctr NA 0 NA
## Datesenttocompany.minute.fctr NA 0 NA
## Datesenttocompany.second.fctr NA 0 NA
## cor.high.X freqRatio
## Datereceived.last8.log1p <NA> 1.109428
## Datesenttocompany.last8.log1p Datereceived.last8.log1p 1.109428
## Datereceived.last4.log1p Datereceived.last8.log1p 1.131868
## Datesenttocompany.last4.log1p Datereceived.last4.log1p 1.131868
## Datereceived.last2.log1p <NA> 1.735692
## Datesenttocompany.last2.log1p Datereceived.last2.log1p 1.735692
## Datereceived.last16.log1p Datereceived.last8.log1p 1.096698
## Datesenttocompany.last16.log1p Datereceived.last16.log1p 1.096698
## Datereceived.last32.log1p <NA> 1.244681
## Datesenttocompany.last32.log1p Datereceived.last32.log1p 1.244681
## .pos <NA> 1.000000
## gCompany.fctr <NA> 1.789357
## gTags.fctr <NA> 13.529412
## Sent.Recd.Dys.nexp <NA> 1.136364
## Datereceived.zoo <NA> 1.566667
## Datesenttocompany.zoo <NA> 1.566667
## Timelyresponse.fctr <NA> 85.939394
## Rgn.Dvn.fctr <NA> 1.274472
## Datereceived.year.fctr <NA> 2.380000
## Datesenttocompany.year.fctr Datereceived.year.fctr 2.380000
## ComplaintID Datereceived.year.fctr 1.000000
## Datereceived.POSIX <NA> 1.566667
## Datesenttocompany.POSIX <NA> 1.566667
## Datereceived.hlday <NA> 94.633333
## Datesenttocompany.hlday Datereceived.hlday 94.633333
## Datereceived.wkday.fctr <NA> 1.087719
## Datesenttocompany.wkday.fctr <NA> 1.087719
## gCConsent.fctr <NA> 1.155955
## .rnorm <NA> 1.000000
## Datereceived.wkend <NA> 6.925414
## Datesenttocompany.wkend Datereceived.wkend 6.925414
## Channel.fctr <NA> 5.260982
## gSPrd.fctr <NA> 3.944118
## .clusterid <NA> 2.213997
## .clusterid.fctr <NA> 2.213997
## Datereceived.month.fctr Datereceived.juliandate 3.323144
## Datesenttocompany.month.fctr Datereceived.month.fctr 3.323144
## Datereceived.juliandate <NA> 1.532258
## Datesenttocompany.juliandate Datereceived.juliandate 1.532258
## Datereceived.date.fctr <NA> 1.330341
## Datesenttocompany.date.fctr Datereceived.date.fctr 1.330341
## Sent.Recd.Dys Sent.Recd.Dys.log1p 1.136364
## Sent.Recd.Dys.root2 Sent.Recd.Dys.log1p 1.136364
## Sent.Recd.Dys.log1p <NA> 1.136364
## Response.fctr <NA> 1.531350
## Datereceived.day.minutes <NA> 0.000000
## Datereceived.hour.fctr <NA> 0.000000
## Datereceived.minute.fctr <NA> 0.000000
## Datereceived.second.fctr <NA> 0.000000
## Datesenttocompany.day.minutes <NA> 0.000000
## Datesenttocompany.hour.fctr <NA> 0.000000
## Datesenttocompany.minute.fctr <NA> 0.000000
## Datesenttocompany.second.fctr <NA> 0.000000
## percentUnique zeroVar nzv
## Datereceived.last8.log1p 0.34855350 FALSE FALSE
## Datesenttocompany.last8.log1p 0.34855350 FALSE FALSE
## Datereceived.last4.log1p 0.24398745 FALSE FALSE
## Datesenttocompany.last4.log1p 0.24398745 FALSE FALSE
## Datereceived.last2.log1p 0.20913210 FALSE FALSE
## Datesenttocompany.last2.log1p 0.20913210 FALSE FALSE
## Datereceived.last16.log1p 0.52283025 FALSE FALSE
## Datesenttocompany.last16.log1p 0.52283025 FALSE FALSE
## Datereceived.last32.log1p 0.80167306 FALSE FALSE
## Datesenttocompany.last32.log1p 0.80167306 FALSE FALSE
## .pos 100.00000000 FALSE FALSE
## gCompany.fctr 0.31369815 FALSE FALSE
## gTags.fctr 0.13942140 FALSE FALSE
## Sent.Recd.Dys.nexp 3.10212618 FALSE FALSE
## Datereceived.zoo 22.62112234 FALSE FALSE
## Datesenttocompany.zoo 22.62112234 FALSE FALSE
## Timelyresponse.fctr 0.06971070 FALSE TRUE
## Rgn.Dvn.fctr 0.34855350 FALSE FALSE
## Datereceived.year.fctr 0.10456605 FALSE FALSE
## Datesenttocompany.year.fctr 0.10456605 FALSE FALSE
## ComplaintID 100.00000000 FALSE FALSE
## Datereceived.POSIX 23.42279540 FALSE FALSE
## Datesenttocompany.POSIX 23.42279540 FALSE FALSE
## Datereceived.hlday 0.06971070 FALSE TRUE
## Datesenttocompany.hlday 0.06971070 FALSE TRUE
## Datereceived.wkday.fctr 0.24398745 FALSE FALSE
## Datesenttocompany.wkday.fctr 0.24398745 FALSE FALSE
## gCConsent.fctr 0.13942140 FALSE FALSE
## .rnorm 100.00000000 FALSE FALSE
## Datereceived.wkend 0.06971070 FALSE FALSE
## Datesenttocompany.wkend 0.06971070 FALSE FALSE
## Channel.fctr 0.17427675 FALSE FALSE
## gSPrd.fctr 0.24398745 FALSE FALSE
## .clusterid 0.13942140 FALSE FALSE
## .clusterid.fctr 0.13942140 FALSE FALSE
## Datereceived.month.fctr 0.41826420 FALSE FALSE
## Datesenttocompany.month.fctr 0.41826420 FALSE FALSE
## Datereceived.juliandate 12.37364936 FALSE FALSE
## Datesenttocompany.juliandate 12.37364936 FALSE FALSE
## Datereceived.date.fctr 0.17427675 FALSE FALSE
## Datesenttocompany.date.fctr 0.17427675 FALSE FALSE
## Sent.Recd.Dys 3.10212618 FALSE FALSE
## Sent.Recd.Dys.root2 3.10212618 FALSE FALSE
## Sent.Recd.Dys.log1p 3.10212618 FALSE FALSE
## Response.fctr 0.13942140 FALSE FALSE
## Datereceived.day.minutes 0.03485535 TRUE TRUE
## Datereceived.hour.fctr 0.03485535 TRUE TRUE
## Datereceived.minute.fctr 0.03485535 TRUE TRUE
## Datereceived.second.fctr 0.03485535 TRUE TRUE
## Datesenttocompany.day.minutes 0.03485535 TRUE TRUE
## Datesenttocompany.hour.fctr 0.03485535 TRUE TRUE
## Datesenttocompany.minute.fctr 0.03485535 TRUE TRUE
## Datesenttocompany.second.fctr 0.03485535 TRUE TRUE
## is.cor.y.abs.low
## Datereceived.last8.log1p FALSE
## Datesenttocompany.last8.log1p FALSE
## Datereceived.last4.log1p FALSE
## Datesenttocompany.last4.log1p FALSE
## Datereceived.last2.log1p FALSE
## Datesenttocompany.last2.log1p FALSE
## Datereceived.last16.log1p FALSE
## Datesenttocompany.last16.log1p FALSE
## Datereceived.last32.log1p FALSE
## Datesenttocompany.last32.log1p FALSE
## .pos FALSE
## gCompany.fctr FALSE
## gTags.fctr FALSE
## Sent.Recd.Dys.nexp FALSE
## Datereceived.zoo FALSE
## Datesenttocompany.zoo FALSE
## Timelyresponse.fctr FALSE
## Rgn.Dvn.fctr FALSE
## Datereceived.year.fctr FALSE
## Datesenttocompany.year.fctr FALSE
## ComplaintID FALSE
## Datereceived.POSIX FALSE
## Datesenttocompany.POSIX FALSE
## Datereceived.hlday FALSE
## Datesenttocompany.hlday FALSE
## Datereceived.wkday.fctr TRUE
## Datesenttocompany.wkday.fctr TRUE
## gCConsent.fctr TRUE
## .rnorm FALSE
## Datereceived.wkend FALSE
## Datesenttocompany.wkend FALSE
## Channel.fctr FALSE
## gSPrd.fctr FALSE
## .clusterid FALSE
## .clusterid.fctr FALSE
## Datereceived.month.fctr FALSE
## Datesenttocompany.month.fctr FALSE
## Datereceived.juliandate FALSE
## Datesenttocompany.juliandate FALSE
## Datereceived.date.fctr FALSE
## Datesenttocompany.date.fctr FALSE
## Sent.Recd.Dys FALSE
## Sent.Recd.Dys.root2 FALSE
## Sent.Recd.Dys.log1p FALSE
## Response.fctr FALSE
## Datereceived.day.minutes NA
## Datereceived.hour.fctr NA
## Datereceived.minute.fctr NA
## Datereceived.second.fctr NA
## Datesenttocompany.day.minutes NA
## Datesenttocompany.hour.fctr NA
## Datesenttocompany.minute.fctr NA
## Datesenttocompany.second.fctr NA## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## cor.y exclude.as.feat cor.y.abs
## Timelyresponse.fctr 0.05434099 0 0.05434099
## Datereceived.hlday 0.01364085 0 0.01364085
## Datesenttocompany.hlday 0.01364085 0 0.01364085
## Datereceived.day.minutes NA 1 NA
## Datereceived.hour.fctr NA 0 NA
## Datereceived.minute.fctr NA 0 NA
## Datereceived.second.fctr NA 0 NA
## Datesenttocompany.day.minutes NA 1 NA
## Datesenttocompany.hour.fctr NA 0 NA
## Datesenttocompany.minute.fctr NA 0 NA
## Datesenttocompany.second.fctr NA 0 NA
## cor.high.X freqRatio percentUnique
## Timelyresponse.fctr <NA> 85.93939 0.06971070
## Datereceived.hlday <NA> 94.63333 0.06971070
## Datesenttocompany.hlday Datereceived.hlday 94.63333 0.06971070
## Datereceived.day.minutes <NA> 0.00000 0.03485535
## Datereceived.hour.fctr <NA> 0.00000 0.03485535
## Datereceived.minute.fctr <NA> 0.00000 0.03485535
## Datereceived.second.fctr <NA> 0.00000 0.03485535
## Datesenttocompany.day.minutes <NA> 0.00000 0.03485535
## Datesenttocompany.hour.fctr <NA> 0.00000 0.03485535
## Datesenttocompany.minute.fctr <NA> 0.00000 0.03485535
## Datesenttocompany.second.fctr <NA> 0.00000 0.03485535
## zeroVar nzv is.cor.y.abs.low
## Timelyresponse.fctr FALSE TRUE FALSE
## Datereceived.hlday FALSE TRUE FALSE
## Datesenttocompany.hlday FALSE TRUE FALSE
## Datereceived.day.minutes TRUE TRUE NA
## Datereceived.hour.fctr TRUE TRUE NA
## Datereceived.minute.fctr TRUE TRUE NA
## Datereceived.second.fctr TRUE TRUE NA
## Datesenttocompany.day.minutes TRUE TRUE NA
## Datesenttocompany.hour.fctr TRUE TRUE NA
## Datesenttocompany.minute.fctr TRUE TRUE NA
## Datesenttocompany.second.fctr TRUE TRUE NA
## [1] "numeric data missing in : "
## CDisputed.fctr
## 165
## [1] "numeric data w/ 0s in : "
## Sent.Recd.Dys Datereceived.wkday.fctr
## 151 143
## Datereceived.wkend Datereceived.hlday
## 2658 3004
## Datereceived.hour.fctr Datereceived.minute.fctr
## 3034 3034
## Datereceived.second.fctr Datereceived.day.minutes
## 3034 3034
## Datesenttocompany.wkday.fctr Datesenttocompany.wkend
## 143 2658
## Datesenttocompany.hlday Datesenttocompany.hour.fctr
## 3004 3034
## Datesenttocompany.minute.fctr Datesenttocompany.second.fctr
## 3034 3034
## Datesenttocompany.day.minutes Datereceived.last2.log1p
## 3034 1767
## Datereceived.last4.log1p Datereceived.last8.log1p
## 1038 594
## Datereceived.last16.log1p Datereceived.last32.log1p
## 419 215
## Datesenttocompany.last2.log1p Datesenttocompany.last4.log1p
## 1767 1038
## Datesenttocompany.last8.log1p Datesenttocompany.last16.log1p
## 594 419
## Datesenttocompany.last32.log1p
## 215
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Datereceived Product
## 0 0
## Subproduct Issue
## 0 0
## Subissue Consumercomplaintnarrative
## 3034 1940
## Companypublicresponse Company
## 2350 0
## State ZIPcode
## 34 34
## Tags Consumerconsentprovided
## 2679 61
## Submittedvia Datesenttocompany
## 0 0
## Companyresponsetoconsumer Timelyresponse
## 0 0
## Consumerdisputed gCompany
## 165 0
## Rgn.Dvn gTags
## 0 0
## gCConsent Channel
## 0 0
## Response .lcn
## 0 165## [1] "glb_feats_df:"## [1] 53 12## id exclude.as.feat rsp_var
## CDisputed.fctr CDisputed.fctr TRUE TRUE## id cor.y exclude.as.feat cor.y.abs
## ComplaintID ComplaintID 0.02064725 FALSE 0.02064725
## CDisputed.fctr CDisputed.fctr NA TRUE NA
## cor.high.X freqRatio percentUnique zeroVar
## ComplaintID Datereceived.year.fctr 1 100 FALSE
## CDisputed.fctr <NA> NA NA NA
## nzv is.cor.y.abs.low interaction.feat
## ComplaintID FALSE FALSE <NA>
## CDisputed.fctr NA NA <NA>
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## ComplaintID 1.176977e-27 FALSE TRUE NA
## CDisputed.fctr NA NA NA TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end elapsed
## 1 select.features 1 0 0 4.371 7.982 3.611
## 2 fit.models 2 0 0 7.982 NA NAStep 2.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 8.811 NA NA# load(paste0(glbOut$pfx, "dsk.RData"))
glbgetModelSelectFormula <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
glbgetDisplayModelsDf <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#glbgetDisplayModelsDf()
# this needs to be in mydsutils.R as lclgetPredictions (functionality needed for utils)
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
#factor(levels(df[, glb_rsp_var])[ # changed to glbObsAll since tileDf won't have it
factor(levels(glbObsAll[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(glbObsAll[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
if (glb_rsp_var %in% names(df)) {
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
if (glb_rsp_var %in% names(df)) {
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
}
return(df)
}
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 8.811 8.834 0.024
## 2 fit.models_0_MFO 1 1 myMFO_classfr 8.835 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: MFO###myMFO_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.372000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.8465562 0.1534438
## [1] "MFO.val:"
## [1] "N"
## [1] "myfit_mdl: train complete: 0.589000 secs"
## parameter
## 1 none
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Warning in if (mdl_specs_lst[["train.method"]] %in% c("glm"))
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used## [1] "myfit_mdl: train diagnostics complete: 0.591000 secs"## Loading required namespace: pROC## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8465562 0.1534438
## 2 0.8465562 0.1534438
## 3 0.8465562 0.1534438
## 4 0.8465562 0.1534438
## 5 0.8465562 0.1534438
## 6 0.8465562 0.1534438## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Warning in doTryCatch(return(expr), name, parentenv, handler): unable to load shared object '/Library/Frameworks/R.framework/Resources/modules//R_X11.so':
## dlopen(/Library/Frameworks/R.framework/Resources/modules//R_X11.so, 6): Library not loaded: /opt/X11/lib/libSM.6.dylib
## Referenced from: /Library/Frameworks/R.framework/Resources/modules//R_X11.so
## Reason: image not found## Could not load tcltk. Will use slower R code instead.## Loading required package: RSQLite## Loading required package: DBI## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score Accuracy g.score
## 5 0.20 0 0.8465562 0
## 6 0.25 0 0.8465562 0
## 7 0.30 0 0.8465562 0
## 8 0.35 0 0.8465562 0
## 9 0.40 0 0.8465562 0
## 10 0.45 0 0.8465562 0
## 11 0.50 0 0.8465562 0
## 12 0.55 0 0.8465562 0
## 13 0.60 0 0.8465562 0
## 14 0.65 0 0.8465562 0
## 15 0.70 0 0.8465562 0
## 16 0.75 0 0.8465562 0
## 17 0.80 0 0.8465562 0
## 18 0.85 0 0.8465562 0
## 19 0.90 0 0.8465562 0
## 20 0.95 0 0.8465562 0
## 21 1.00 0 0.8465562 0
## Prediction
## Reference N Y
## N 1942 0
## Y 352 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.465562e-01 0.000000e+00 8.311483e-01 8.610732e-01 8.465562e-01
## AccuracyPValue McnemarPValue
## 5.142198e-01 4.231759e-78
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8465562 0.1534438
## 2 0.8465562 0.1534438
## 3 0.8465562 0.1534438
## 4 0.8465562 0.1534438
## 5 0.8465562 0.1534438
## 6 0.8465562 0.1534438
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score Accuracy g.score
## 5 0.20 0 0.8521739 0
## 6 0.25 0 0.8521739 0
## 7 0.30 0 0.8521739 0
## 8 0.35 0 0.8521739 0
## 9 0.40 0 0.8521739 0
## 10 0.45 0 0.8521739 0
## 11 0.50 0 0.8521739 0
## 12 0.55 0 0.8521739 0
## 13 0.60 0 0.8521739 0
## 14 0.65 0 0.8521739 0
## 15 0.70 0 0.8521739 0
## 16 0.75 0 0.8521739 0
## 17 0.80 0 0.8521739 0
## 18 0.85 0 0.8521739 0
## 19 0.90 0 0.8521739 0
## 20 0.95 0 0.8521739 0
## 21 1.00 0 0.8521739 0## Prediction
## Reference N Y
## N 490 0
## Y 85 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.521739e-01 0.000000e+00 8.204899e-01 8.801833e-01 8.521739e-01
## AccuracyPValue McnemarPValue
## 5.288965e-01 8.156649e-20
## [1] "myfit_mdl: predict complete: 5.399000 secs"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm 0 0.209
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.004 0.5 1 0
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.8465562
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8311483 0.8610732 0
## min.log.loss.mean.fit max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## 1 0.4286364 0.5 1 0
## max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.5 0 0.8521739
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8204899 0.8801833 0
## min.log.loss.mean.OOB
## 1 0.4190423
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8465562 0.1534438
## 2 0.8465562 0.1534438
## 3 0.8465562 0.1534438
## 4 0.8465562 0.1534438
## 5 0.8465562 0.1534438
## 6 0.8465562 0.1534438
## [1] "myfit_mdl: exit: 5.434000 secs"
## label step_major step_minor label_minor bgn end
## 2 fit.models_0_MFO 1 1 myMFO_classfr 8.835 14.272
## 3 fit.models_0_Random 1 2 myrandom_classfr 14.273 NA
## elapsed
## 2 5.437
## 3 NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Random###myrandom_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.364000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.554000 secs"
## parameter
## 1 none
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Warning in if (mdl_specs_lst[["train.method"]] %in% c("glm"))
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used
## [1] "myfit_mdl: train diagnostics complete: 0.556000 secs"
## [1] "in Random.Classifier$prob"
## Prediction
## Reference N Y
## N 1942 0
## Y 352 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.465562e-01 0.000000e+00 8.311483e-01 8.610732e-01 8.465562e-01
## AccuracyPValue McnemarPValue
## 5.142198e-01 4.231759e-78
## [1] "in Random.Classifier$prob"
## Prediction
## Reference N Y
## N 490 0
## Y 85 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.521739e-01 0.000000e+00 8.204899e-01 8.801833e-01 8.521739e-01
## AccuracyPValue McnemarPValue
## 5.288965e-01 8.156649e-20
## [1] "myfit_mdl: predict complete: 5.343000 secs"
## id feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.186 0.002 0.5304308
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8619979 0.1988636 0.5155782 0.85
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0 0.8465562 0.8311483
## max.AccuracyUpper.fit max.Kappa.fit min.log.loss.mean.fit
## 1 0.8610732 0 0.5819998
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.4971188 0.8530612 0.1411765 0.4994598
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.85 0 0.8521739
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8204899 0.8801833 0
## min.log.loss.mean.OOB
## 1 0.6328939
## [1] "in Random.Classifier$prob"
## [1] "myfit_mdl: exit: 5.763000 secs"# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## bgn end elapsed
## 3 14.273 20.043 5.77
## 4 20.044 NA NAret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indepVar: Response.fctr,Datereceived.last8.log1p"
## [1] "myfit_mdl: setup complete: 0.628000 secs"## Loading required package: glmnet## Loading required package: Matrix## Loaded glmnet 2.0-5## Fitting alpha = 0.1, lambda = 0.00123 on full training set
## [1] "myfit_mdl: train complete: 1.177000 secs"
## alpha lambda
## 1 0.1 0.001234118
## Length Class Mode
## a0 63 -none- numeric
## beta 378 dgCMatrix S4
## df 63 -none- numeric
## dim 2 -none- numeric
## lambda 63 -none- numeric
## dev.ratio 63 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 6 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -2.31371676
## Datereceived.last8.log1p
## 0.09437238
## Response.fctrClosed
## -0.75787216
## Response.fctrClosedwithmonetaryrelief
## -1.00113018
## Response.fctrClosedwithnon-monetaryrelief
## -1.20016668
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"
## [2] "Datereceived.last8.log1p"
## [3] "Response.fctrClosed"
## [4] "Response.fctrClosedwithmonetaryrelief"
## [5] "Response.fctrClosedwithnon-monetaryrelief"
## [6] "Response.fctrInprogress"
## [7] "Response.fctrUntimelyresponse"
## [1] "myfit_mdl: train diagnostics complete: 1.236000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score Accuracy g.score
## 7 0.30 0 0.8465562 0
## 8 0.35 0 0.8465562 0
## 9 0.40 0 0.8465562 0
## 10 0.45 0 0.8465562 0
## 11 0.50 0 0.8465562 0
## 12 0.55 0 0.8465562 0
## 13 0.60 0 0.8465562 0
## 14 0.65 0 0.8465562 0
## 15 0.70 0 0.8465562 0
## 16 0.75 0 0.8465562 0
## 17 0.80 0 0.8465562 0
## 18 0.85 0 0.8465562 0
## 19 0.90 0 0.8465562 0
## 20 0.95 0 0.8465562 0
## 21 1.00 0 0.8465562 0
## Prediction
## Reference N Y
## N 1942 0
## Y 352 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.465562e-01 0.000000e+00 8.311483e-01 8.610732e-01 8.465562e-01
## AccuracyPValue McnemarPValue
## 5.142198e-01 4.231759e-78
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score Accuracy g.score
## 7 0.30 0 0.8521739 0
## 8 0.35 0 0.8521739 0
## 9 0.40 0 0.8521739 0
## 10 0.45 0 0.8521739 0
## 11 0.50 0 0.8521739 0
## 12 0.55 0 0.8521739 0
## 13 0.60 0 0.8521739 0
## 14 0.65 0 0.8521739 0
## 15 0.70 0 0.8521739 0
## 16 0.75 0 0.8521739 0
## 17 0.80 0 0.8521739 0
## 18 0.85 0 0.8521739 0
## 19 0.90 0 0.8521739 0
## 20 0.95 0 0.8521739 0
## 21 1.00 0 0.8521739 0
## Prediction
## Reference N Y
## N 490 0
## Y 85 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.521739e-01 0.000000e+00 8.204899e-01 8.801833e-01 8.521739e-01
## AccuracyPValue McnemarPValue
## 5.288965e-01 8.156649e-20
## [1] "myfit_mdl: predict complete: 5.976000 secs"
## id feats
## 1 Max.cor.Y.rcv.1X1###glmnet Response.fctr,Datereceived.last8.log1p
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.545 0.028
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5 1 0 0.6926559
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8465562
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8311483 0.8610732 0
## min.log.loss.mean.fit max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## 1 0.3956106 0.5 1 0
## max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6620888 0.5 0 0.8521739
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8204899 0.8801833 0
## min.log.loss.mean.OOB
## 1 0.396265
## [1] "myfit_mdl: exit: 6.021000 secs"if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y##rcv#rpart"
## [1] " indepVar: Response.fctr,Datereceived.last8.log1p"
## [1] "myfit_mdl: setup complete: 0.649000 secs"## Loading required package: rpart## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 2.172000 secs"
## cp
## 1 0## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 2294
##
## CP nsplit rel error
## 1 0 0 1
##
## Node number 1: 2294 observations
## predicted class=N expected loss=0.1534438 P(node) =1
## class counts: 1942 352
## probabilities: 0.847 0.153
##
## n= 2294
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 2294 352 N (0.8465562 0.1534438) *
## [1] "myfit_mdl: train diagnostics complete: 2.245000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score Accuracy g.score
## 5 0.20 0 0.8465562 0
## 6 0.25 0 0.8465562 0
## 7 0.30 0 0.8465562 0
## 8 0.35 0 0.8465562 0
## 9 0.40 0 0.8465562 0
## 10 0.45 0 0.8465562 0
## 11 0.50 0 0.8465562 0
## 12 0.55 0 0.8465562 0
## 13 0.60 0 0.8465562 0
## 14 0.65 0 0.8465562 0
## 15 0.70 0 0.8465562 0
## 16 0.75 0 0.8465562 0
## 17 0.80 0 0.8465562 0
## 18 0.85 0 0.8465562 0
## 19 0.90 0 0.8465562 0
## 20 0.95 0 0.8465562 0
## 21 1.00 0 0.8465562 0
## Prediction
## Reference N Y
## N 1942 0
## Y 352 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.465562e-01 0.000000e+00 8.311483e-01 8.610732e-01 8.465562e-01
## AccuracyPValue McnemarPValue
## 5.142198e-01 4.231759e-78
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score Accuracy g.score
## 5 0.20 0 0.8521739 0
## 6 0.25 0 0.8521739 0
## 7 0.30 0 0.8521739 0
## 8 0.35 0 0.8521739 0
## 9 0.40 0 0.8521739 0
## 10 0.45 0 0.8521739 0
## 11 0.50 0 0.8521739 0
## 12 0.55 0 0.8521739 0
## 13 0.60 0 0.8521739 0
## 14 0.65 0 0.8521739 0
## 15 0.70 0 0.8521739 0
## 16 0.75 0 0.8521739 0
## 17 0.80 0 0.8521739 0
## 18 0.85 0 0.8521739 0
## 19 0.90 0 0.8521739 0
## 20 0.95 0 0.8521739 0
## 21 1.00 0 0.8521739 0
## Prediction
## Reference N Y
## N 490 0
## Y 85 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.521739e-01 0.000000e+00 8.204899e-01 8.801833e-01 8.521739e-01
## AccuracyPValue McnemarPValue
## 5.288965e-01 8.156649e-20
## [1] "myfit_mdl: predict complete: 6.816000 secs"
## id feats
## 1 Max.cor.Y##rcv#rpart Response.fctr,Datereceived.last8.log1p
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.518 0.012
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5 1 0 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.846557
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8311483 0.8610732 0
## min.log.loss.mean.fit max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## 1 0.4286364 0.5 1 0
## max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.5 0 0.8521739
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8204899 0.8801833 0
## min.log.loss.mean.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.4190423 0.001268446 0
## [1] "myfit_mdl: exit: 6.855000 secs"if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## Warning in grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\
## \.", : argument 'pattern' has length > 1 and only the first element will be
## usedif ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## Warning in grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep =
## ""), : argument 'pattern' has length > 1 and only the first element will be
## used## label step_major step_minor label_minor bgn
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet 20.044
## 5 fit.models_0_Max.cor.Y.Time.Lag 1 4 glmnet 32.949
## end elapsed
## 4 32.949 12.905
## 5 NA NA## Warning in grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep =
## ""), : argument 'pattern' has length > 1 and only the first element will be
## used## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y.Time.Lag##rcv#glmnet"
## [1] " indepVar: Response.fctr,Datereceived.last8.log1p,Datereceived.last2.log1p,Datereceived.last4.log1p,Datereceived.last8.log1p,Datereceived.last16.log1p,Datereceived.last32.log1p"
## [1] "myfit_mdl: setup complete: 0.649000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0266 on full training set
## [1] "myfit_mdl: train complete: 3.269000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y.Time.Lag", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y.Time.Lag", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 66 -none- numeric
## beta 660 dgCMatrix S4
## df 66 -none- numeric
## dim 2 -none- numeric
## lambda 66 -none- numeric
## dev.ratio 66 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 10 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -2.456486518
## Datereceived.last16.log1p
## 0.025290767
## Datereceived.last2.log1p
## 0.028843596
## Datereceived.last32.log1p
## 0.007323618
## Datereceived.last4.log1p
## 0.017719043
## Datereceived.last8.log1p
## 0.031818768
## Response.fctrClosed
## -0.427428564
## Response.fctrClosedwithmonetaryrelief
## -0.707306703
## Response.fctrClosedwithnon-monetaryrelief
## -0.740698272
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -2.456784371
## Datereceived.last16.log1p
## 0.025793869
## Datereceived.last2.log1p
## 0.029287652
## Datereceived.last32.log1p
## 0.006793292
## Datereceived.last4.log1p
## 0.017558474
## Datereceived.last8.log1p
## 0.032422397
## Response.fctrClosed
## -0.455957896
## Response.fctrClosedwithmonetaryrelief
## -0.726890951
## Response.fctrClosedwithnon-monetaryrelief
## -0.766614013
## [1] "myfit_mdl: train diagnostics complete: 3.659000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score Accuracy g.score
## 7 0.30 0 0.8465562 0
## 8 0.35 0 0.8465562 0
## 9 0.40 0 0.8465562 0
## 10 0.45 0 0.8465562 0
## 11 0.50 0 0.8465562 0
## 12 0.55 0 0.8465562 0
## 13 0.60 0 0.8465562 0
## 14 0.65 0 0.8465562 0
## 15 0.70 0 0.8465562 0
## 16 0.75 0 0.8465562 0
## 17 0.80 0 0.8465562 0
## 18 0.85 0 0.8465562 0
## 19 0.90 0 0.8465562 0
## 20 0.95 0 0.8465562 0
## 21 1.00 0 0.8465562 0
## Prediction
## Reference N Y
## N 1942 0
## Y 352 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.465562e-01 0.000000e+00 8.311483e-01 8.610732e-01 8.465562e-01
## AccuracyPValue McnemarPValue
## 5.142198e-01 4.231759e-78
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score Accuracy g.score
## 7 0.30 0 0.8521739 0
## 8 0.35 0 0.8521739 0
## 9 0.40 0 0.8521739 0
## 10 0.45 0 0.8521739 0
## 11 0.50 0 0.8521739 0
## 12 0.55 0 0.8521739 0
## 13 0.60 0 0.8521739 0
## 14 0.65 0 0.8521739 0
## 15 0.70 0 0.8521739 0
## 16 0.75 0 0.8521739 0
## 17 0.80 0 0.8521739 0
## 18 0.85 0 0.8521739 0
## 19 0.90 0 0.8521739 0
## 20 0.95 0 0.8521739 0
## 21 1.00 0 0.8521739 0
## Prediction
## Reference N Y
## N 490 0
## Y 85 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.521739e-01 0.000000e+00 8.204899e-01 8.801833e-01 8.521739e-01
## AccuracyPValue McnemarPValue
## 5.288965e-01 8.156649e-20
## [1] "myfit_mdl: predict complete: 8.855000 secs"
## id
## 1 Max.cor.Y.Time.Lag##rcv#glmnet
## feats
## 1 Response.fctr,Datereceived.last8.log1p,Datereceived.last2.log1p,Datereceived.last4.log1p,Datereceived.last8.log1p,Datereceived.last16.log1p,Datereceived.last32.log1p
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 20 2.615 0.048
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5 1 0 0.7025954
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.846557
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8311483 0.8610732 0
## min.log.loss.mean.fit max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## 1 0.3944061 0.5 1 0
## max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6766867 0.5 0 0.8521739
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8204899 0.8801833 0
## min.log.loss.mean.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.3928984 0.001293567 0
## [1] "myfit_mdl: exit: 8.899000 secs"if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# # Interactions.High.cor.Y - Failing for CFPB
# if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
# subset(glb_feats_df, nzv)$id)) > 0) {
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
# label.minor = "glmnet")
#
# ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
# id.prefix="Interact.High.cor.Y",
# type=glb_model_type, trainControl.method="repeatedcv",
# trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
# trainControl.classProbs = glb_is_classification,
# trainControl.summaryFunction = glbMdlMetricSummaryFn,
# trainControl.blockParallel = glbMdlSequential,
# train.metric = glbMdlMetricSummary,
# train.maximize = glbMdlMetricMaximize,
# train.method="glmnet")),
# indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
# }
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor bgn
## 5 fit.models_0_Max.cor.Y.Time.Lag 1 4 glmnet 32.949
## 6 fit.models_0_Low.cor.X 1 5 glmnet 41.868
## end elapsed
## 5 41.867 8.918
## 6 NA NAindepVar <- mygetIndepVar(glb_feats_df)
indepVar <- setdiff(indepVar, unique(glb_feats_df$cor.high.X))
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Low.cor.X##rcv#glmnet"
## [1] " indepVar: Datesenttocompany.last8.log1p,Datesenttocompany.last4.log1p,Datesenttocompany.last2.log1p,Datesenttocompany.last16.log1p,Datesenttocompany.last32.log1p,.pos,gCompany.fctr,gTags.fctr,Sent.Recd.Dys.nexp,Rgn.Dvn.fctr,Datesenttocompany.year.fctr,ComplaintID,Datereceived.wkday.fctr,Datesenttocompany.wkday.fctr,gCConsent.fctr,.rnorm,Datesenttocompany.wkend,Channel.fctr,gSPrd.fctr,Datesenttocompany.month.fctr,Datesenttocompany.juliandate,Datesenttocompany.date.fctr,Sent.Recd.Dys,Sent.Recd.Dys.root2,Response.fctr,gSPrd.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.638000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.00573 on full training set
## [1] "myfit_mdl: train complete: 14.232000 secs"
## Length Class Mode
## a0 71 -none- numeric
## beta 18247 dgCMatrix S4
## df 71 -none- numeric
## dim 2 -none- numeric
## lambda 71 -none- numeric
## dev.ratio 71 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 257 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -2.433548e+00
## .pos
## 1.267093e-06
## Channel.fctrFax
## -1.104907e+00
## Channel.fctrPhone
## -5.175991e-01
## Channel.fctrPostalmail
## -3.054622e-02
## Channel.fctrReferral
## -4.109098e-01
## ComplaintID
## 2.348800e-08
## Datereceived.wkday.fctr2
## -2.543153e-02
## Datereceived.wkday.fctr4
## 5.059309e-03
## Datesenttocompany.date.fctr(7,13]
## 6.443015e-02
## Datesenttocompany.date.fctr(25,31]
## -2.787219e-02
## Datesenttocompany.last16.log1p
## 5.242160e-03
## Datesenttocompany.last2.log1p
## 4.871687e-02
## Datesenttocompany.last4.log1p
## 1.528235e-02
## Datesenttocompany.last8.log1p
## 2.712306e-02
## Datesenttocompany.month.fctr03
## -1.914886e-01
## Datesenttocompany.month.fctr05
## 2.256885e-01
## Datesenttocompany.month.fctr06
## -6.190687e-01
## Datesenttocompany.month.fctr07
## 1.902271e-01
## Datesenttocompany.month.fctr09
## 1.055777e-01
## Datesenttocompany.month.fctr11
## -2.170876e-01
## Datesenttocompany.month.fctr12
## 1.921579e-01
## Datesenttocompany.wkday.fctr2
## -3.871961e-03
## Datesenttocompany.wkend
## -3.035000e-01
## Response.fctrClosed
## -1.789888e-01
## Response.fctrClosedwithmonetaryrelief
## -8.941904e-01
## Response.fctrClosedwithnon-monetaryrelief
## -9.422449e-01
## Rgn.Dvn.fctrMW#EastNorthCentral
## -2.080807e-01
## Rgn.Dvn.fctrMW#WestNorthCentral
## -1.689175e-01
## Rgn.Dvn.fctrSH#EastSouthCentral
## -1.644112e-01
## Rgn.Dvn.fctrWT#Mountain
## 1.404571e-01
## Rgn.Dvn.fctrWT#Pacific
## 7.908540e-02
## Sent.Recd.Dys
## -4.054143e-06
## Sent.Recd.Dys.root2
## -4.996583e-02
## gCConsent.fctrOther
## 2.738017e-01
## gCompany.fctrAmex
## 5.999896e-01
## gCompany.fctrBlackhawkNetworkHoldingsInc.
## 3.179058e-01
## gCompany.fctrCitibank
## 3.531965e-02
## gCompany.fctrNetSpendCorporation,aTSYSCompany
## -5.164812e-01
## gCompany.fctrPayPalHoldings,Inc.
## 9.015014e-02
## gSPrd.fctrMobilewallet
## 2.270989e-01
## gSPrd.fctrGiftormerchantcard
## 2.801459e-02
## gSPrd.fctrPayrollcard
## -1.289396e-01
## gTags.fctrOlderAmerican
## 2.592657e-01
## gTags.fctrOlderAmericanServicemember
## 6.365123e-02
## gTags.fctrServicemember
## 4.736706e-01
## gSPrd.fctrOther:.clusterid.fctr2
## 4.147463e-01
## gSPrd.fctrMobilewallet:.clusterid.fctr3
## 7.911245e-01
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3
## -2.048482e-01
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -2.461641e+00
## .pos
## 1.288490e-06
## Channel.fctrFax
## -1.218382e+00
## Channel.fctrPhone
## -5.378485e-01
## Channel.fctrPostalmail
## -5.885045e-02
## Channel.fctrReferral
## -4.362777e-01
## ComplaintID
## 3.613044e-08
## Datereceived.wkday.fctr2
## -3.383480e-02
## Datereceived.wkday.fctr4
## 7.969319e-03
## Datesenttocompany.date.fctr(7,13]
## 7.000321e-02
## Datesenttocompany.date.fctr(25,31]
## -3.738569e-02
## Datesenttocompany.last16.log1p
## 5.841918e-03
## Datesenttocompany.last2.log1p
## 4.962428e-02
## Datesenttocompany.last4.log1p
## 1.542214e-02
## Datesenttocompany.last8.log1p
## 2.673389e-02
## Datesenttocompany.month.fctr03
## -2.077124e-01
## Datesenttocompany.month.fctr05
## 2.272972e-01
## Datesenttocompany.month.fctr06
## -6.419009e-01
## Datesenttocompany.month.fctr07
## 1.918444e-01
## Datesenttocompany.month.fctr09
## 1.115205e-01
## Datesenttocompany.month.fctr11
## -2.314703e-01
## Datesenttocompany.month.fctr12
## 1.996210e-01
## Datesenttocompany.wkday.fctr2
## -5.674348e-03
## Datesenttocompany.wkday.fctr4
## 1.521616e-05
## Datesenttocompany.wkend
## -3.247318e-01
## Response.fctrClosed
## -2.060992e-01
## Response.fctrClosedwithmonetaryrelief
## -9.071151e-01
## Response.fctrClosedwithnon-monetaryrelief
## -9.633833e-01
## Rgn.Dvn.fctrMW#EastNorthCentral
## -2.189782e-01
## Rgn.Dvn.fctrMW#WestNorthCentral
## -1.896482e-01
## Rgn.Dvn.fctrSH#EastSouthCentral
## -1.870465e-01
## Rgn.Dvn.fctrWT#Mountain
## 1.488105e-01
## Rgn.Dvn.fctrWT#Pacific
## 8.042002e-02
## Sent.Recd.Dys
## -2.482952e-04
## Sent.Recd.Dys.root2
## -4.846512e-02
## gCConsent.fctrOther
## 2.884425e-01
## gCompany.fctrAmex
## 6.060368e-01
## gCompany.fctrBlackhawkNetworkHoldingsInc.
## 3.275146e-01
## gCompany.fctrCitibank
## 4.074131e-02
## gCompany.fctrNetSpendCorporation,aTSYSCompany
## -5.272676e-01
## gCompany.fctrPayPalHoldings,Inc.
## 1.099305e-01
## gSPrd.fctrMobilewallet
## 2.319685e-01
## gSPrd.fctrGiftormerchantcard
## 3.712782e-02
## gSPrd.fctrPayrollcard
## -1.349860e-01
## gTags.fctrOlderAmerican
## 2.740282e-01
## gTags.fctrOlderAmericanServicemember
## 8.641216e-02
## gTags.fctrServicemember
## 4.878184e-01
## gSPrd.fctrOther:.clusterid.fctr2
## 4.453500e-01
## gSPrd.fctrIDprepaidcard:.clusterid.fctr2
## -2.745050e-03
## gSPrd.fctrMobilewallet:.clusterid.fctr3
## 8.102407e-01
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3
## -2.105951e-01
## [1] "myfit_mdl: train diagnostics complete: 14.612000 secs"
## Prediction
## Reference N Y
## N 1914 28
## Y 300 52
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.570183e-01 1.949913e-01 8.420201e-01 8.710982e-01 8.465562e-01
## AccuracyPValue McnemarPValue
## 8.576118e-02 1.272127e-50
## Prediction
## Reference N Y
## N 488 2
## Y 82 3
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.539130e-01 5.108055e-02 8.223606e-01 8.817721e-01 8.521739e-01
## AccuracyPValue McnemarPValue
## 4.820691e-01 6.718449e-18
## [1] "myfit_mdl: predict complete: 21.598000 secs"
## id
## 1 Low.cor.X##rcv#glmnet
## feats
## 1 Datesenttocompany.last8.log1p,Datesenttocompany.last4.log1p,Datesenttocompany.last2.log1p,Datesenttocompany.last16.log1p,Datesenttocompany.last32.log1p,.pos,gCompany.fctr,gTags.fctr,Sent.Recd.Dys.nexp,Rgn.Dvn.fctr,Datesenttocompany.year.fctr,ComplaintID,Datereceived.wkday.fctr,Datesenttocompany.wkday.fctr,gCConsent.fctr,.rnorm,Datesenttocompany.wkend,Channel.fctr,gSPrd.fctr,Datesenttocompany.month.fctr,Datesenttocompany.juliandate,Datesenttocompany.date.fctr,Sent.Recd.Dys,Sent.Recd.Dys.root2,Response.fctr,gSPrd.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 13.573 0.287
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5246714 0.9953656 0.05397727 0.778257
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.2407407 0.8484475
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8420201 0.8710982 0.0609904
## min.log.loss.mean.fit max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## 1 0.3619785 0.5156062 0.9959184 0.03529412
## max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7136134 0.5 0.06666667 0.853913
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8223606 0.8817721 0.05108055
## min.log.loss.mean.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.3849163 0.005830864 0.05136076
## [1] "myfit_mdl: exit: 21.719000 secs"fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 6 fit.models_0_Low.cor.X 1 5 glmnet 41.868 63.615
## 7 fit.models_0_end 1 6 teardown 63.616 NA
## elapsed
## 6 21.747
## 7 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 2 fit.models 2 0 0 7.982 63.627 55.645
## 3 fit.models 2 1 1 63.628 NA NAif (!is.null(glbChunks$first) && (glbChunks$first == "fit.models_1") &&
(is.null(knitr::opts_current$get(name = 'label')))) # not knitting
myloadChunk(glbChunks$inpFilePathName, keepSpec = c(NULL), dropSpec = c(NULL))## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 66.502 NA NA## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 66.502 66.511
## 2 fit.models_1_All.X 1 1 setup 66.511 NA
## elapsed
## 1 0.009
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 66.511 66.516
## 3 fit.models_1_All.X 1 2 glmnet 66.516 NA
## elapsed
## 2 0.005
## 3 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: All.X##rcv#glmnet"
## [1] " indepVar: Datereceived.last8.log1p,Datesenttocompany.last8.log1p,Datereceived.last4.log1p,Datesenttocompany.last4.log1p,Datereceived.last2.log1p,Datesenttocompany.last2.log1p,Datereceived.last16.log1p,Datesenttocompany.last16.log1p,Datereceived.last32.log1p,Datesenttocompany.last32.log1p,.pos,gCompany.fctr,gTags.fctr,Sent.Recd.Dys.nexp,Rgn.Dvn.fctr,Datereceived.year.fctr,Datesenttocompany.year.fctr,ComplaintID,Datereceived.wkday.fctr,Datesenttocompany.wkday.fctr,gCConsent.fctr,.rnorm,Datereceived.wkend,Datesenttocompany.wkend,Channel.fctr,gSPrd.fctr,Datereceived.month.fctr,Datesenttocompany.month.fctr,Datereceived.juliandate,Datesenttocompany.juliandate,Datereceived.date.fctr,Datesenttocompany.date.fctr,Sent.Recd.Dys,Sent.Recd.Dys.root2,Sent.Recd.Dys.log1p,Response.fctr,gSPrd.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.919000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.00573 on full training set
## [1] "myfit_mdl: train complete: 19.396000 secs"
## Length Class Mode
## a0 71 -none- numeric
## beta 20022 dgCMatrix S4
## df 71 -none- numeric
## dim 2 -none- numeric
## lambda 71 -none- numeric
## dev.ratio 71 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 282 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -2.419910e+00
## .pos
## 1.270245e-06
## Channel.fctrFax
## -1.100421e+00
## Channel.fctrPhone
## -5.174989e-01
## Channel.fctrPostalmail
## -2.917941e-02
## Channel.fctrReferral
## -4.105974e-01
## ComplaintID
## 2.387479e-08
## Datereceived.date.fctr(7,13]
## 5.549457e-02
## Datereceived.date.fctr(25,31]
## -2.180554e-02
## Datereceived.last16.log1p
## 3.509841e-03
## Datereceived.last2.log1p
## 2.736109e-02
## Datereceived.last4.log1p
## 8.457981e-03
## Datereceived.last8.log1p
## 1.443510e-02
## Datereceived.month.fctr03
## -1.290143e-01
## Datereceived.month.fctr05
## 1.478965e-01
## Datereceived.month.fctr06
## -3.707619e-01
## Datereceived.month.fctr07
## 1.004336e-01
## Datereceived.month.fctr09
## 7.072848e-02
## Datereceived.month.fctr11
## -1.387415e-01
## Datereceived.month.fctr12
## 1.220996e-01
## Datereceived.wkday.fctr2
## -2.143593e-02
## Datereceived.wkday.fctr4
## 4.484184e-03
## Datereceived.wkend
## -1.964110e-01
## Datesenttocompany.date.fctr(7,13]
## 9.645623e-03
## Datesenttocompany.date.fctr(25,31]
## -5.337761e-03
## Datesenttocompany.last16.log1p
## 4.804400e-04
## Datesenttocompany.last2.log1p
## 2.170553e-02
## Datesenttocompany.last4.log1p
## 6.547603e-03
## Datesenttocompany.last8.log1p
## 1.402838e-02
## Datesenttocompany.month.fctr03
## -6.540598e-02
## Datesenttocompany.month.fctr05
## 7.784004e-02
## Datesenttocompany.month.fctr06
## -2.540588e-01
## Datesenttocompany.month.fctr07
## 8.864728e-02
## Datesenttocompany.month.fctr09
## 3.413979e-02
## Datesenttocompany.month.fctr11
## -8.082078e-02
## Datesenttocompany.month.fctr12
## 7.049100e-02
## Datesenttocompany.wkday.fctr2
## -8.808256e-03
## Datesenttocompany.wkday.fctr4
## 7.687700e-04
## Datesenttocompany.wkend
## -1.104052e-01
## Response.fctrClosed
## -1.760776e-01
## Response.fctrClosedwithmonetaryrelief
## -8.937495e-01
## Response.fctrClosedwithnon-monetaryrelief
## -9.411886e-01
## Rgn.Dvn.fctrMW#EastNorthCentral
## -2.076588e-01
## Rgn.Dvn.fctrMW#WestNorthCentral
## -1.684006e-01
## Rgn.Dvn.fctrSH#EastSouthCentral
## -1.639738e-01
## Rgn.Dvn.fctrWT#Mountain
## 1.414948e-01
## Rgn.Dvn.fctrWT#Pacific
## 7.931496e-02
## Sent.Recd.Dys
## -7.492570e-04
## Sent.Recd.Dys.log1p
## -4.136294e-02
## Sent.Recd.Dys.root2
## -2.226308e-02
## gCConsent.fctrOther
## 2.739421e-01
## gCompany.fctrAmex
## 5.998988e-01
## gCompany.fctrBlackhawkNetworkHoldingsInc.
## 3.174423e-01
## gCompany.fctrCitibank
## 3.479079e-02
## gCompany.fctrNetSpendCorporation,aTSYSCompany
## -5.176567e-01
## gCompany.fctrPayPalHoldings,Inc.
## 8.920291e-02
## gSPrd.fctrMobilewallet
## 2.264725e-01
## gSPrd.fctrGiftormerchantcard
## 2.822442e-02
## gSPrd.fctrPayrollcard
## -1.292153e-01
## gTags.fctrOlderAmerican
## 2.598133e-01
## gTags.fctrOlderAmericanServicemember
## 6.364291e-02
## gTags.fctrServicemember
## 4.740235e-01
## gSPrd.fctrOther:.clusterid.fctr2
## 4.153102e-01
## gSPrd.fctrMobilewallet:.clusterid.fctr3
## 7.924461e-01
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3
## -2.036339e-01
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -2.455210e+00
## .pos
## 1.291876e-06
## Channel.fctrFax
## -1.214681e+00
## Channel.fctrPhone
## -5.379010e-01
## Channel.fctrPostalmail
## -5.780989e-02
## Channel.fctrReferral
## -4.362680e-01
## ComplaintID
## 3.650745e-08
## Datereceived.date.fctr(7,13]
## 5.993537e-02
## Datereceived.date.fctr(25,31]
## -2.926023e-02
## Datereceived.last16.log1p
## 4.281708e-03
## Datereceived.last2.log1p
## 2.787167e-02
## Datereceived.last4.log1p
## 8.557289e-03
## Datereceived.last8.log1p
## 1.419809e-02
## Datereceived.month.fctr03
## -1.385143e-01
## Datereceived.month.fctr05
## 1.494618e-01
## Datereceived.month.fctr06
## -3.827170e-01
## Datereceived.month.fctr07
## 1.016817e-01
## Datereceived.month.fctr09
## 7.456160e-02
## Datereceived.month.fctr11
## -1.462918e-01
## Datereceived.month.fctr12
## 1.263628e-01
## Datereceived.wkday.fctr2
## -2.862393e-02
## Datereceived.wkday.fctr4
## 7.172346e-03
## Datereceived.wkend
## -2.087693e-01
## Datesenttocompany.date.fctr(7,13]
## 1.076576e-02
## Datesenttocompany.date.fctr(25,31]
## -7.737384e-03
## Datesenttocompany.last16.log1p
## 4.321540e-04
## Datesenttocompany.last2.log1p
## 2.207996e-02
## Datesenttocompany.last4.log1p
## 6.611831e-03
## Datesenttocompany.last8.log1p
## 1.381145e-02
## Datesenttocompany.month.fctr03
## -7.171952e-02
## Datesenttocompany.month.fctr05
## 7.794843e-02
## Datesenttocompany.month.fctr06
## -2.645513e-01
## Datesenttocompany.month.fctr07
## 8.927039e-02
## Datesenttocompany.month.fctr09
## 3.641733e-02
## Datesenttocompany.month.fctr11
## -8.748546e-02
## Datesenttocompany.month.fctr12
## 7.393871e-02
## Datesenttocompany.wkday.fctr2
## -1.168792e-02
## Datesenttocompany.wkday.fctr4
## 7.959302e-04
## Datesenttocompany.wkend
## -1.192483e-01
## Response.fctrClosed
## -2.033891e-01
## Response.fctrClosedwithmonetaryrelief
## -9.068406e-01
## Response.fctrClosedwithnon-monetaryrelief
## -9.626498e-01
## Rgn.Dvn.fctrMW#EastNorthCentral
## -2.187633e-01
## Rgn.Dvn.fctrMW#WestNorthCentral
## -1.892977e-01
## Rgn.Dvn.fctrSH#EastSouthCentral
## -1.868548e-01
## Rgn.Dvn.fctrWT#Mountain
## 1.496359e-01
## Rgn.Dvn.fctrWT#Pacific
## 8.060493e-02
## Sent.Recd.Dys
## -1.194853e-03
## Sent.Recd.Dys.log1p
## -3.575199e-02
## Sent.Recd.Dys.root2
## -2.216281e-02
## gCConsent.fctrOther
## 2.886341e-01
## gCompany.fctrAmex
## 6.059865e-01
## gCompany.fctrBlackhawkNetworkHoldingsInc.
## 3.271687e-01
## gCompany.fctrCitibank
## 4.021240e-02
## gCompany.fctrNetSpendCorporation,aTSYSCompany
## -5.281495e-01
## gCompany.fctrPayPalHoldings,Inc.
## 1.091706e-01
## gSPrd.fctrMobilewallet
## 2.317166e-01
## gSPrd.fctrGiftormerchantcard
## 3.734556e-02
## gSPrd.fctrPayrollcard
## -1.350751e-01
## gTags.fctrOlderAmerican
## 2.744928e-01
## gTags.fctrOlderAmericanServicemember
## 8.619736e-02
## gTags.fctrServicemember
## 4.880524e-01
## gSPrd.fctrOther:.clusterid.fctr2
## 4.461189e-01
## gSPrd.fctrIDprepaidcard:.clusterid.fctr2
## -1.569848e-03
## gSPrd.fctrMobilewallet:.clusterid.fctr3
## 8.111637e-01
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3
## -2.096471e-01
## [1] "myfit_mdl: train diagnostics complete: 19.757000 secs"
## Prediction
## Reference N Y
## N 1914 28
## Y 300 52
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.570183e-01 1.949913e-01 8.420201e-01 8.710982e-01 8.465562e-01
## AccuracyPValue McnemarPValue
## 8.576118e-02 1.272127e-50
## Prediction
## Reference N Y
## N 490 0
## Y 84 1
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.539130e-01 1.988636e-02 8.223606e-01 8.817721e-01 8.521739e-01
## AccuracyPValue McnemarPValue
## 4.820691e-01 1.352695e-19
## [1] "myfit_mdl: predict complete: 26.100000 secs"
## id
## 1 All.X##rcv#glmnet
## feats
## 1 Datereceived.last8.log1p,Datesenttocompany.last8.log1p,Datereceived.last4.log1p,Datesenttocompany.last4.log1p,Datereceived.last2.log1p,Datesenttocompany.last2.log1p,Datereceived.last16.log1p,Datesenttocompany.last16.log1p,Datereceived.last32.log1p,Datesenttocompany.last32.log1p,.pos,gCompany.fctr,gTags.fctr,Sent.Recd.Dys.nexp,Rgn.Dvn.fctr,Datereceived.year.fctr,Datesenttocompany.year.fctr,ComplaintID,Datereceived.wkday.fctr,Datesenttocompany.wkday.fctr,gCConsent.fctr,.rnorm,Datereceived.wkend,Datesenttocompany.wkend,Channel.fctr,gSPrd.fctr,Datereceived.month.fctr,Datesenttocompany.month.fctr,Datereceived.juliandate,Datesenttocompany.juliandate,Datereceived.date.fctr,Datesenttocompany.date.fctr,Sent.Recd.Dys,Sent.Recd.Dys.root2,Sent.Recd.Dys.log1p,Response.fctr,gSPrd.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 18.452 0.341
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5246714 0.9953656 0.05397727 0.7783082
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.2407407 0.8481571
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8420201 0.8710982 0.05929954
## min.log.loss.mean.fit max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## 1 0.3619515 0.5145858 0.9938776 0.03529412
## max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7134214 0.6 0.02325581 0.853913
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8223606 0.8817721 0.01988636
## min.log.loss.mean.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.3850093 0.006025779 0.05058534
## [1] "myfit_mdl: exit: 26.204000 secs"
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 66.516 92.723
## 4 fit.models_1_All.X 1 3 glm 92.724 NA
## elapsed
## 3 26.207
## 4 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: All.X##rcv#glm"
## [1] " indepVar: Datereceived.last8.log1p,Datesenttocompany.last8.log1p,Datereceived.last4.log1p,Datesenttocompany.last4.log1p,Datereceived.last2.log1p,Datesenttocompany.last2.log1p,Datereceived.last16.log1p,Datesenttocompany.last16.log1p,Datereceived.last32.log1p,Datesenttocompany.last32.log1p,.pos,gCompany.fctr,gTags.fctr,Sent.Recd.Dys.nexp,Rgn.Dvn.fctr,Datereceived.year.fctr,Datesenttocompany.year.fctr,ComplaintID,Datereceived.wkday.fctr,Datesenttocompany.wkday.fctr,gCConsent.fctr,.rnorm,Datereceived.wkend,Datesenttocompany.wkend,Channel.fctr,gSPrd.fctr,Datereceived.month.fctr,Datesenttocompany.month.fctr,Datereceived.juliandate,Datesenttocompany.juliandate,Datereceived.date.fctr,Datesenttocompany.date.fctr,Sent.Recd.Dys,Sent.Recd.Dys.root2,Sent.Recd.Dys.log1p,Response.fctr,gSPrd.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.602000 secs"
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 48.620000 secs"
## parameter
## 1 none
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7188 -0.6002 -0.3601 -0.2056 3.0879
##
## Coefficients: (197 not defined because of singularities)
## Estimate
## (Intercept) 1.421e+00
## .pos 1.437e-06
## .rnorm -1.143e-02
## Channel.fctrFax -1.545e+01
## Channel.fctrPhone -1.210e+00
## Channel.fctrPostalmail -8.130e-01
## Channel.fctrReferral -1.155e+00
## ComplaintID -8.930e-06
## `Datereceived.date.fctr(7,13]` -6.501e-02
## `Datereceived.date.fctr(13,19]` -3.882e-01
## `Datereceived.date.fctr(19,25]` -4.453e-01
## `Datereceived.date.fctr(25,31]` -6.874e-01
## Datereceived.juliandate 3.407e-02
## Datereceived.last16.log1p 7.147e-02
## Datereceived.last2.log1p 5.770e-02
## Datereceived.last32.log1p -2.251e-02
## Datereceived.last4.log1p 1.816e-02
## Datereceived.last8.log1p 4.128e-02
## Datereceived.month.fctr02 -6.428e-01
## Datereceived.month.fctr03 -1.465e+00
## Datereceived.month.fctr04 -1.714e+00
## Datereceived.month.fctr05 -1.925e+00
## Datereceived.month.fctr06 -3.728e+00
## Datereceived.month.fctr07 -3.165e+00
## Datereceived.month.fctr08 -3.907e+00
## Datereceived.month.fctr09 -4.414e+00
## Datereceived.month.fctr10 -4.948e+00
## Datereceived.month.fctr11 -6.223e+00
## Datereceived.month.fctr12 -6.230e+00
## Datereceived.wkday.fctr1 9.295e-01
## Datereceived.wkday.fctr2 7.169e-01
## Datereceived.wkday.fctr3 8.111e-01
## Datereceived.wkday.fctr4 9.476e-01
## Datereceived.wkday.fctr5 9.909e-01
## Datereceived.wkday.fctr6 5.204e-01
## Datereceived.wkend NA
## Datereceived.year.fctr2015 5.354e+00
## Datereceived.year.fctr2016 1.038e+01
## `Datesenttocompany.date.fctr(7,13]` NA
## `Datesenttocompany.date.fctr(13,19]` NA
## `Datesenttocompany.date.fctr(19,25]` NA
## `Datesenttocompany.date.fctr(25,31]` NA
## Datesenttocompany.juliandate NA
## Datesenttocompany.last16.log1p NA
## Datesenttocompany.last2.log1p NA
## Datesenttocompany.last32.log1p NA
## Datesenttocompany.last4.log1p NA
## Datesenttocompany.last8.log1p NA
## Datesenttocompany.month.fctr02 NA
## Datesenttocompany.month.fctr03 NA
## Datesenttocompany.month.fctr04 NA
## Datesenttocompany.month.fctr05 NA
## Datesenttocompany.month.fctr06 NA
## Datesenttocompany.month.fctr07 NA
## Datesenttocompany.month.fctr08 NA
## Datesenttocompany.month.fctr09 NA
## Datesenttocompany.month.fctr10 NA
## Datesenttocompany.month.fctr11 NA
## Datesenttocompany.month.fctr12 NA
## Datesenttocompany.wkday.fctr1 NA
## Datesenttocompany.wkday.fctr2 NA
## Datesenttocompany.wkday.fctr3 NA
## Datesenttocompany.wkday.fctr4 NA
## Datesenttocompany.wkday.fctr5 NA
## Datesenttocompany.wkday.fctr6 NA
## Datesenttocompany.wkend NA
## Datesenttocompany.year.fctr2015 NA
## Datesenttocompany.year.fctr2016 NA
## Response.fctrClosed -4.618e-01
## Response.fctrClosedwithmonetaryrelief -1.078e+00
## `Response.fctrClosedwithnon-monetaryrelief` -1.224e+00
## Response.fctrInprogress NA
## Response.fctrUntimelyresponse NA
## `Rgn.Dvn.fctrMW#EastNorthCentral` -4.049e-01
## `Rgn.Dvn.fctrMW#WestNorthCentral` -4.834e-01
## `Rgn.Dvn.fctrMW#WestNorthCentral#MidAtlantic` -3.518e-02
## `Rgn.Dvn.fctrMW#WestNorthCentral#NewEngland` -1.963e-01
## `Rgn.Dvn.fctrOT#Other` 6.135e-02
## `Rgn.Dvn.fctrSH#EastSouthCentral` -5.202e-01
## `Rgn.Dvn.fctrSH#WestSouthCentral` -7.564e-02
## `Rgn.Dvn.fctrWT#Mountain` 1.959e-01
## `Rgn.Dvn.fctrWT#Pacific` 6.079e-02
## Sent.Recd.Dys -8.425e-02
## Sent.Recd.Dys.log1p -2.034e+00
## Sent.Recd.Dys.nexp -4.216e-01
## Sent.Recd.Dys.root2 1.605e+00
## gCConsent.fctrConsentnotprovided -5.668e-01
## gCConsent.fctrConsentprovided -5.378e-01
## gCConsent.fctrOther -4.871e-02
## gCompany.fctrAmex 7.407e-01
## gCompany.fctrBlackhawkNetworkHoldingsInc. 5.162e-01
## gCompany.fctrCitibank 1.777e-01
## gCompany.fctrComerica 1.137e-01
## `gCompany.fctrNetSpendCorporation,aTSYSCompany` -5.990e-01
## gCompany.fctrOther 4.039e-02
## `gCompany.fctrPayPalHoldings,Inc.` 4.157e-01
## gCompany.fctrU.S.Bancorp -5.801e-02
## gSPrd.fctrVehicleloan NA
## gSPrd.fctrCheckingaccount NA
## gSPrd.fctrConventionalfixedmortgage NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.)` NA
## gSPrd.fctrIdonotknow NA
## gSPrd.fctr NA
## gSPrd.fctrOthermortgage NA
## gSPrd.fctrCreditcard NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM)` NA
## `gSPrd.fctrOtherbankproduct/service` NA
## `gSPrd.fctrNon-federalstudentloan` NA
## gSPrd.fctrFHAmortgage NA
## `gSPrd.fctr(CD)Certificateofdeposit` NA
## gSPrd.fctrHomeequityloanorlineofcredit NA
## gSPrd.fctrVehiclelease NA
## gSPrd.fctrSavingsaccount NA
## gSPrd.fctrFederalstudentloan NA
## gSPrd.fctrMortgage NA
## gSPrd.fctrReversemortgage NA
## gSPrd.fctrAuto NA
## gSPrd.fctrInstallmentloan NA
## gSPrd.fctrMedical NA
## gSPrd.fctrVAmortgage NA
## `gSPrd.fctrDomestic(US)moneytransfer` NA
## gSPrd.fctrInternationalmoneytransfer NA
## gSPrd.fctrPersonallineofcredit NA
## gSPrd.fctrSecondmortgage NA
## gSPrd.fctrCashingacheckwithoutanaccount NA
## gSPrd.fctrPaydayloan NA
## gSPrd.fctrCheckcashing NA
## gSPrd.fctrMobilewallet 2.997e-01
## gSPrd.fctrGovernmentbenefitpaymentcard 1.918e-01
## gSPrd.fctrGiftormerchantcard 2.626e-01
## gSPrd.fctrGeneralpurposecard -1.207e-02
## gSPrd.fctrTitleloan NA
## gSPrd.fctrForeigncurrencyexchange NA
## gSPrd.fctrRefundanticipationcheck NA
## gSPrd.fctrDebtsettlement NA
## gSPrd.fctrPayrollcard -3.810e-01
## gSPrd.fctrIDprepaidcard -4.777e-02
## gSPrd.fctrCreditrepair NA
## gSPrd.fctrPawnloan NA
## gSPrd.fctrMoneyorder NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks` NA
## gSPrd.fctrFederalstudentloanservicing NA
## gTags.fctrOlderAmerican 4.383e-01
## gTags.fctrOlderAmericanServicemember 4.169e-01
## gTags.fctrServicemember 6.558e-01
## `gSPrd.fctrOther:.clusterid.fctr2` 8.010e-01
## `gSPrd.fctrVehicleloan:.clusterid.fctr2` NA
## `gSPrd.fctrCheckingaccount:.clusterid.fctr2` NA
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr2` NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr2` NA
## `gSPrd.fctrIdonotknow:.clusterid.fctr2` NA
## `gSPrd.fctr:.clusterid.fctr2` NA
## `gSPrd.fctrOthermortgage:.clusterid.fctr2` NA
## `gSPrd.fctrCreditcard:.clusterid.fctr2` NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr2` NA
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr2` NA
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr2` NA
## `gSPrd.fctrFHAmortgage:.clusterid.fctr2` NA
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr2` NA
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr2` NA
## `gSPrd.fctrVehiclelease:.clusterid.fctr2` NA
## `gSPrd.fctrSavingsaccount:.clusterid.fctr2` NA
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr2` NA
## `gSPrd.fctrMortgage:.clusterid.fctr2` NA
## `gSPrd.fctrReversemortgage:.clusterid.fctr2` NA
## `gSPrd.fctrAuto:.clusterid.fctr2` NA
## `gSPrd.fctrInstallmentloan:.clusterid.fctr2` NA
## `gSPrd.fctrMedical:.clusterid.fctr2` NA
## `gSPrd.fctrVAmortgage:.clusterid.fctr2` NA
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr2` NA
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr2` NA
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr2` NA
## `gSPrd.fctrSecondmortgage:.clusterid.fctr2` NA
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr2` NA
## `gSPrd.fctrPaydayloan:.clusterid.fctr2` NA
## `gSPrd.fctrCheckcashing:.clusterid.fctr2` NA
## `gSPrd.fctrMobilewallet:.clusterid.fctr2` 7.034e-02
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr2` -1.703e-01
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr2` -2.885e-01
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr2` 6.054e-01
## `gSPrd.fctrTitleloan:.clusterid.fctr2` NA
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr2` NA
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr2` NA
## `gSPrd.fctrDebtsettlement:.clusterid.fctr2` NA
## `gSPrd.fctrPayrollcard:.clusterid.fctr2` 1.340e+00
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr2` -3.636e-01
## `gSPrd.fctrCreditrepair:.clusterid.fctr2` NA
## `gSPrd.fctrPawnloan:.clusterid.fctr2` NA
## `gSPrd.fctrMoneyorder:.clusterid.fctr2` NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr2` NA
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr2` NA
## `gSPrd.fctrOther:.clusterid.fctr3` 3.150e-01
## `gSPrd.fctrVehicleloan:.clusterid.fctr3` NA
## `gSPrd.fctrCheckingaccount:.clusterid.fctr3` NA
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr3` NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr3` NA
## `gSPrd.fctrIdonotknow:.clusterid.fctr3` NA
## `gSPrd.fctr:.clusterid.fctr3` NA
## `gSPrd.fctrOthermortgage:.clusterid.fctr3` NA
## `gSPrd.fctrCreditcard:.clusterid.fctr3` NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr3` NA
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr3` NA
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr3` NA
## `gSPrd.fctrFHAmortgage:.clusterid.fctr3` NA
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr3` NA
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr3` NA
## `gSPrd.fctrVehiclelease:.clusterid.fctr3` NA
## `gSPrd.fctrSavingsaccount:.clusterid.fctr3` NA
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr3` NA
## `gSPrd.fctrMortgage:.clusterid.fctr3` NA
## `gSPrd.fctrReversemortgage:.clusterid.fctr3` NA
## `gSPrd.fctrAuto:.clusterid.fctr3` NA
## `gSPrd.fctrInstallmentloan:.clusterid.fctr3` NA
## `gSPrd.fctrMedical:.clusterid.fctr3` NA
## `gSPrd.fctrVAmortgage:.clusterid.fctr3` NA
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr3` NA
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr3` NA
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr3` NA
## `gSPrd.fctrSecondmortgage:.clusterid.fctr3` NA
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr3` NA
## `gSPrd.fctrPaydayloan:.clusterid.fctr3` NA
## `gSPrd.fctrCheckcashing:.clusterid.fctr3` NA
## `gSPrd.fctrMobilewallet:.clusterid.fctr3` 1.105e+00
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr3` NA
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr3` -1.403e-01
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr3` -1.600e-01
## `gSPrd.fctrTitleloan:.clusterid.fctr3` NA
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr3` NA
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr3` NA
## `gSPrd.fctrDebtsettlement:.clusterid.fctr3` NA
## `gSPrd.fctrPayrollcard:.clusterid.fctr3` NA
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr3` -1.732e-01
## `gSPrd.fctrCreditrepair:.clusterid.fctr3` NA
## `gSPrd.fctrPawnloan:.clusterid.fctr3` NA
## `gSPrd.fctrMoneyorder:.clusterid.fctr3` NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr3` NA
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr3` NA
## `gSPrd.fctrOther:.clusterid.fctr4` -1.276e+01
## `gSPrd.fctrVehicleloan:.clusterid.fctr4` NA
## `gSPrd.fctrCheckingaccount:.clusterid.fctr4` NA
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr4` NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr4` NA
## `gSPrd.fctrIdonotknow:.clusterid.fctr4` NA
## `gSPrd.fctr:.clusterid.fctr4` NA
## `gSPrd.fctrOthermortgage:.clusterid.fctr4` NA
## `gSPrd.fctrCreditcard:.clusterid.fctr4` NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr4` NA
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr4` NA
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr4` NA
## `gSPrd.fctrFHAmortgage:.clusterid.fctr4` NA
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr4` NA
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr4` NA
## `gSPrd.fctrVehiclelease:.clusterid.fctr4` NA
## `gSPrd.fctrSavingsaccount:.clusterid.fctr4` NA
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr4` NA
## `gSPrd.fctrMortgage:.clusterid.fctr4` NA
## `gSPrd.fctrReversemortgage:.clusterid.fctr4` NA
## `gSPrd.fctrAuto:.clusterid.fctr4` NA
## `gSPrd.fctrInstallmentloan:.clusterid.fctr4` NA
## `gSPrd.fctrMedical:.clusterid.fctr4` NA
## `gSPrd.fctrVAmortgage:.clusterid.fctr4` NA
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr4` NA
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr4` NA
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr4` NA
## `gSPrd.fctrSecondmortgage:.clusterid.fctr4` NA
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr4` NA
## `gSPrd.fctrPaydayloan:.clusterid.fctr4` NA
## `gSPrd.fctrCheckcashing:.clusterid.fctr4` NA
## `gSPrd.fctrMobilewallet:.clusterid.fctr4` NA
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr4` NA
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr4` NA
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr4` NA
## `gSPrd.fctrTitleloan:.clusterid.fctr4` NA
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr4` NA
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr4` NA
## `gSPrd.fctrDebtsettlement:.clusterid.fctr4` NA
## `gSPrd.fctrPayrollcard:.clusterid.fctr4` NA
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr4` NA
## `gSPrd.fctrCreditrepair:.clusterid.fctr4` NA
## `gSPrd.fctrPawnloan:.clusterid.fctr4` NA
## `gSPrd.fctrMoneyorder:.clusterid.fctr4` NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr4` NA
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr4` NA
## Std. Error
## (Intercept) 2.254e+01
## .pos 4.999e-07
## .rnorm 6.477e-02
## Channel.fctrFax 6.363e+02
## Channel.fctrPhone 3.043e-01
## Channel.fctrPostalmail 4.459e-01
## Channel.fctrReferral 2.989e-01
## ComplaintID 3.517e-05
## `Datereceived.date.fctr(7,13]` 3.105e-01
## `Datereceived.date.fctr(13,19]` 5.062e-01
## `Datereceived.date.fctr(19,25]` 7.054e-01
## `Datereceived.date.fctr(25,31]` 9.197e-01
## Datereceived.juliandate 6.484e-02
## Datereceived.last16.log1p 6.204e-02
## Datereceived.last2.log1p 1.554e-02
## Datereceived.last32.log1p 5.497e-02
## Datereceived.last4.log1p 2.022e-02
## Datereceived.last8.log1p 4.503e-02
## Datereceived.month.fctr02 1.204e+00
## Datereceived.month.fctr03 2.207e+00
## Datereceived.month.fctr04 3.320e+00
## Datereceived.month.fctr05 4.416e+00
## Datereceived.month.fctr06 5.553e+00
## Datereceived.month.fctr07 6.643e+00
## Datereceived.month.fctr08 7.781e+00
## Datereceived.month.fctr09 8.898e+00
## Datereceived.month.fctr10 9.992e+00
## Datereceived.month.fctr11 1.116e+01
## Datereceived.month.fctr12 1.224e+01
## Datereceived.wkday.fctr1 3.587e-01
## Datereceived.wkday.fctr2 3.579e-01
## Datereceived.wkday.fctr3 3.571e-01
## Datereceived.wkday.fctr4 3.593e-01
## Datereceived.wkday.fctr5 3.668e-01
## Datereceived.wkday.fctr6 3.951e-01
## Datereceived.wkend NA
## Datereceived.year.fctr2015 1.885e+01
## Datereceived.year.fctr2016 3.805e+01
## `Datesenttocompany.date.fctr(7,13]` NA
## `Datesenttocompany.date.fctr(13,19]` NA
## `Datesenttocompany.date.fctr(19,25]` NA
## `Datesenttocompany.date.fctr(25,31]` NA
## Datesenttocompany.juliandate NA
## Datesenttocompany.last16.log1p NA
## Datesenttocompany.last2.log1p NA
## Datesenttocompany.last32.log1p NA
## Datesenttocompany.last4.log1p NA
## Datesenttocompany.last8.log1p NA
## Datesenttocompany.month.fctr02 NA
## Datesenttocompany.month.fctr03 NA
## Datesenttocompany.month.fctr04 NA
## Datesenttocompany.month.fctr05 NA
## Datesenttocompany.month.fctr06 NA
## Datesenttocompany.month.fctr07 NA
## Datesenttocompany.month.fctr08 NA
## Datesenttocompany.month.fctr09 NA
## Datesenttocompany.month.fctr10 NA
## Datesenttocompany.month.fctr11 NA
## Datesenttocompany.month.fctr12 NA
## Datesenttocompany.wkday.fctr1 NA
## Datesenttocompany.wkday.fctr2 NA
## Datesenttocompany.wkday.fctr3 NA
## Datesenttocompany.wkday.fctr4 NA
## Datesenttocompany.wkday.fctr5 NA
## Datesenttocompany.wkday.fctr6 NA
## Datesenttocompany.wkend NA
## Datesenttocompany.year.fctr2015 NA
## Datesenttocompany.year.fctr2016 NA
## Response.fctrClosed 6.654e-01
## Response.fctrClosedwithmonetaryrelief 1.739e-01
## `Response.fctrClosedwithnon-monetaryrelief` 2.813e-01
## Response.fctrInprogress NA
## Response.fctrUntimelyresponse NA
## `Rgn.Dvn.fctrMW#EastNorthCentral` 2.389e-01
## `Rgn.Dvn.fctrMW#WestNorthCentral` 3.977e-01
## `Rgn.Dvn.fctrMW#WestNorthCentral#MidAtlantic` 2.189e-01
## `Rgn.Dvn.fctrMW#WestNorthCentral#NewEngland` 3.145e-01
## `Rgn.Dvn.fctrOT#Other` 5.934e-01
## `Rgn.Dvn.fctrSH#EastSouthCentral` 4.065e-01
## `Rgn.Dvn.fctrSH#WestSouthCentral` 2.469e-01
## `Rgn.Dvn.fctrWT#Mountain` 2.750e-01
## `Rgn.Dvn.fctrWT#Pacific` 1.972e-01
## Sent.Recd.Dys 1.580e-01
## Sent.Recd.Dys.log1p 3.446e+00
## Sent.Recd.Dys.nexp 6.817e-01
## Sent.Recd.Dys.root2 2.963e+00
## gCConsent.fctrConsentnotprovided 2.909e-01
## gCConsent.fctrConsentprovided 2.674e-01
## gCConsent.fctrOther 4.208e-01
## gCompany.fctrAmex 3.162e-01
## gCompany.fctrBlackhawkNetworkHoldingsInc. 3.813e-01
## gCompany.fctrCitibank 3.875e-01
## gCompany.fctrComerica 4.823e-01
## `gCompany.fctrNetSpendCorporation,aTSYSCompany` 3.668e-01
## gCompany.fctrOther 3.348e-01
## `gCompany.fctrPayPalHoldings,Inc.` 3.914e-01
## gCompany.fctrU.S.Bancorp 3.783e-01
## gSPrd.fctrVehicleloan NA
## gSPrd.fctrCheckingaccount NA
## gSPrd.fctrConventionalfixedmortgage NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.)` NA
## gSPrd.fctrIdonotknow NA
## gSPrd.fctr NA
## gSPrd.fctrOthermortgage NA
## gSPrd.fctrCreditcard NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM)` NA
## `gSPrd.fctrOtherbankproduct/service` NA
## `gSPrd.fctrNon-federalstudentloan` NA
## gSPrd.fctrFHAmortgage NA
## `gSPrd.fctr(CD)Certificateofdeposit` NA
## gSPrd.fctrHomeequityloanorlineofcredit NA
## gSPrd.fctrVehiclelease NA
## gSPrd.fctrSavingsaccount NA
## gSPrd.fctrFederalstudentloan NA
## gSPrd.fctrMortgage NA
## gSPrd.fctrReversemortgage NA
## gSPrd.fctrAuto NA
## gSPrd.fctrInstallmentloan NA
## gSPrd.fctrMedical NA
## gSPrd.fctrVAmortgage NA
## `gSPrd.fctrDomestic(US)moneytransfer` NA
## gSPrd.fctrInternationalmoneytransfer NA
## gSPrd.fctrPersonallineofcredit NA
## gSPrd.fctrSecondmortgage NA
## gSPrd.fctrCashingacheckwithoutanaccount NA
## gSPrd.fctrPaydayloan NA
## gSPrd.fctrCheckcashing NA
## gSPrd.fctrMobilewallet 4.630e-01
## gSPrd.fctrGovernmentbenefitpaymentcard 5.287e-01
## gSPrd.fctrGiftormerchantcard 4.445e-01
## gSPrd.fctrGeneralpurposecard 4.038e-01
## gSPrd.fctrTitleloan NA
## gSPrd.fctrForeigncurrencyexchange NA
## gSPrd.fctrRefundanticipationcheck NA
## gSPrd.fctrDebtsettlement NA
## gSPrd.fctrPayrollcard 4.945e-01
## gSPrd.fctrIDprepaidcard 5.206e-01
## gSPrd.fctrCreditrepair NA
## gSPrd.fctrPawnloan NA
## gSPrd.fctrMoneyorder NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks` NA
## gSPrd.fctrFederalstudentloanservicing NA
## gTags.fctrOlderAmerican 2.854e-01
## gTags.fctrOlderAmericanServicemember 5.506e-01
## gTags.fctrServicemember 2.359e-01
## `gSPrd.fctrOther:.clusterid.fctr2` 5.757e-01
## `gSPrd.fctrVehicleloan:.clusterid.fctr2` NA
## `gSPrd.fctrCheckingaccount:.clusterid.fctr2` NA
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr2` NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr2` NA
## `gSPrd.fctrIdonotknow:.clusterid.fctr2` NA
## `gSPrd.fctr:.clusterid.fctr2` NA
## `gSPrd.fctrOthermortgage:.clusterid.fctr2` NA
## `gSPrd.fctrCreditcard:.clusterid.fctr2` NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr2` NA
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr2` NA
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr2` NA
## `gSPrd.fctrFHAmortgage:.clusterid.fctr2` NA
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr2` NA
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr2` NA
## `gSPrd.fctrVehiclelease:.clusterid.fctr2` NA
## `gSPrd.fctrSavingsaccount:.clusterid.fctr2` NA
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr2` NA
## `gSPrd.fctrMortgage:.clusterid.fctr2` NA
## `gSPrd.fctrReversemortgage:.clusterid.fctr2` NA
## `gSPrd.fctrAuto:.clusterid.fctr2` NA
## `gSPrd.fctrInstallmentloan:.clusterid.fctr2` NA
## `gSPrd.fctrMedical:.clusterid.fctr2` NA
## `gSPrd.fctrVAmortgage:.clusterid.fctr2` NA
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr2` NA
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr2` NA
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr2` NA
## `gSPrd.fctrSecondmortgage:.clusterid.fctr2` NA
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr2` NA
## `gSPrd.fctrPaydayloan:.clusterid.fctr2` NA
## `gSPrd.fctrCheckcashing:.clusterid.fctr2` NA
## `gSPrd.fctrMobilewallet:.clusterid.fctr2` 4.674e-01
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr2` 6.440e-01
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr2` 6.559e-01
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr2` 4.987e-01
## `gSPrd.fctrTitleloan:.clusterid.fctr2` NA
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr2` NA
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr2` NA
## `gSPrd.fctrDebtsettlement:.clusterid.fctr2` NA
## `gSPrd.fctrPayrollcard:.clusterid.fctr2` 8.073e-01
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr2` 1.170e+00
## `gSPrd.fctrCreditrepair:.clusterid.fctr2` NA
## `gSPrd.fctrPawnloan:.clusterid.fctr2` NA
## `gSPrd.fctrMoneyorder:.clusterid.fctr2` NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr2` NA
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr2` NA
## `gSPrd.fctrOther:.clusterid.fctr3` 6.408e-01
## `gSPrd.fctrVehicleloan:.clusterid.fctr3` NA
## `gSPrd.fctrCheckingaccount:.clusterid.fctr3` NA
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr3` NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr3` NA
## `gSPrd.fctrIdonotknow:.clusterid.fctr3` NA
## `gSPrd.fctr:.clusterid.fctr3` NA
## `gSPrd.fctrOthermortgage:.clusterid.fctr3` NA
## `gSPrd.fctrCreditcard:.clusterid.fctr3` NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr3` NA
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr3` NA
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr3` NA
## `gSPrd.fctrFHAmortgage:.clusterid.fctr3` NA
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr3` NA
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr3` NA
## `gSPrd.fctrVehiclelease:.clusterid.fctr3` NA
## `gSPrd.fctrSavingsaccount:.clusterid.fctr3` NA
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr3` NA
## `gSPrd.fctrMortgage:.clusterid.fctr3` NA
## `gSPrd.fctrReversemortgage:.clusterid.fctr3` NA
## `gSPrd.fctrAuto:.clusterid.fctr3` NA
## `gSPrd.fctrInstallmentloan:.clusterid.fctr3` NA
## `gSPrd.fctrMedical:.clusterid.fctr3` NA
## `gSPrd.fctrVAmortgage:.clusterid.fctr3` NA
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr3` NA
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr3` NA
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr3` NA
## `gSPrd.fctrSecondmortgage:.clusterid.fctr3` NA
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr3` NA
## `gSPrd.fctrPaydayloan:.clusterid.fctr3` NA
## `gSPrd.fctrCheckcashing:.clusterid.fctr3` NA
## `gSPrd.fctrMobilewallet:.clusterid.fctr3` 4.991e-01
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr3` NA
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr3` 6.527e-01
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr3` 3.194e-01
## `gSPrd.fctrTitleloan:.clusterid.fctr3` NA
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr3` NA
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr3` NA
## `gSPrd.fctrDebtsettlement:.clusterid.fctr3` NA
## `gSPrd.fctrPayrollcard:.clusterid.fctr3` NA
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr3` 6.368e-01
## `gSPrd.fctrCreditrepair:.clusterid.fctr3` NA
## `gSPrd.fctrPawnloan:.clusterid.fctr3` NA
## `gSPrd.fctrMoneyorder:.clusterid.fctr3` NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr3` NA
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr3` NA
## `gSPrd.fctrOther:.clusterid.fctr4` 5.008e+02
## `gSPrd.fctrVehicleloan:.clusterid.fctr4` NA
## `gSPrd.fctrCheckingaccount:.clusterid.fctr4` NA
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr4` NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr4` NA
## `gSPrd.fctrIdonotknow:.clusterid.fctr4` NA
## `gSPrd.fctr:.clusterid.fctr4` NA
## `gSPrd.fctrOthermortgage:.clusterid.fctr4` NA
## `gSPrd.fctrCreditcard:.clusterid.fctr4` NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr4` NA
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr4` NA
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr4` NA
## `gSPrd.fctrFHAmortgage:.clusterid.fctr4` NA
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr4` NA
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr4` NA
## `gSPrd.fctrVehiclelease:.clusterid.fctr4` NA
## `gSPrd.fctrSavingsaccount:.clusterid.fctr4` NA
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr4` NA
## `gSPrd.fctrMortgage:.clusterid.fctr4` NA
## `gSPrd.fctrReversemortgage:.clusterid.fctr4` NA
## `gSPrd.fctrAuto:.clusterid.fctr4` NA
## `gSPrd.fctrInstallmentloan:.clusterid.fctr4` NA
## `gSPrd.fctrMedical:.clusterid.fctr4` NA
## `gSPrd.fctrVAmortgage:.clusterid.fctr4` NA
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr4` NA
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr4` NA
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr4` NA
## `gSPrd.fctrSecondmortgage:.clusterid.fctr4` NA
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr4` NA
## `gSPrd.fctrPaydayloan:.clusterid.fctr4` NA
## `gSPrd.fctrCheckcashing:.clusterid.fctr4` NA
## `gSPrd.fctrMobilewallet:.clusterid.fctr4` NA
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr4` NA
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr4` NA
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr4` NA
## `gSPrd.fctrTitleloan:.clusterid.fctr4` NA
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr4` NA
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr4` NA
## `gSPrd.fctrDebtsettlement:.clusterid.fctr4` NA
## `gSPrd.fctrPayrollcard:.clusterid.fctr4` NA
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr4` NA
## `gSPrd.fctrCreditrepair:.clusterid.fctr4` NA
## `gSPrd.fctrPawnloan:.clusterid.fctr4` NA
## `gSPrd.fctrMoneyorder:.clusterid.fctr4` NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr4` NA
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr4` NA
## z value
## (Intercept) 0.063
## .pos 2.876
## .rnorm -0.176
## Channel.fctrFax -0.024
## Channel.fctrPhone -3.976
## Channel.fctrPostalmail -1.823
## Channel.fctrReferral -3.864
## ComplaintID -0.254
## `Datereceived.date.fctr(7,13]` -0.209
## `Datereceived.date.fctr(13,19]` -0.767
## `Datereceived.date.fctr(19,25]` -0.631
## `Datereceived.date.fctr(25,31]` -0.747
## Datereceived.juliandate 0.525
## Datereceived.last16.log1p 1.152
## Datereceived.last2.log1p 3.712
## Datereceived.last32.log1p -0.409
## Datereceived.last4.log1p 0.898
## Datereceived.last8.log1p 0.917
## Datereceived.month.fctr02 -0.534
## Datereceived.month.fctr03 -0.664
## Datereceived.month.fctr04 -0.516
## Datereceived.month.fctr05 -0.436
## Datereceived.month.fctr06 -0.671
## Datereceived.month.fctr07 -0.476
## Datereceived.month.fctr08 -0.502
## Datereceived.month.fctr09 -0.496
## Datereceived.month.fctr10 -0.495
## Datereceived.month.fctr11 -0.558
## Datereceived.month.fctr12 -0.509
## Datereceived.wkday.fctr1 2.591
## Datereceived.wkday.fctr2 2.003
## Datereceived.wkday.fctr3 2.271
## Datereceived.wkday.fctr4 2.637
## Datereceived.wkday.fctr5 2.702
## Datereceived.wkday.fctr6 1.317
## Datereceived.wkend NA
## Datereceived.year.fctr2015 0.284
## Datereceived.year.fctr2016 0.273
## `Datesenttocompany.date.fctr(7,13]` NA
## `Datesenttocompany.date.fctr(13,19]` NA
## `Datesenttocompany.date.fctr(19,25]` NA
## `Datesenttocompany.date.fctr(25,31]` NA
## Datesenttocompany.juliandate NA
## Datesenttocompany.last16.log1p NA
## Datesenttocompany.last2.log1p NA
## Datesenttocompany.last32.log1p NA
## Datesenttocompany.last4.log1p NA
## Datesenttocompany.last8.log1p NA
## Datesenttocompany.month.fctr02 NA
## Datesenttocompany.month.fctr03 NA
## Datesenttocompany.month.fctr04 NA
## Datesenttocompany.month.fctr05 NA
## Datesenttocompany.month.fctr06 NA
## Datesenttocompany.month.fctr07 NA
## Datesenttocompany.month.fctr08 NA
## Datesenttocompany.month.fctr09 NA
## Datesenttocompany.month.fctr10 NA
## Datesenttocompany.month.fctr11 NA
## Datesenttocompany.month.fctr12 NA
## Datesenttocompany.wkday.fctr1 NA
## Datesenttocompany.wkday.fctr2 NA
## Datesenttocompany.wkday.fctr3 NA
## Datesenttocompany.wkday.fctr4 NA
## Datesenttocompany.wkday.fctr5 NA
## Datesenttocompany.wkday.fctr6 NA
## Datesenttocompany.wkend NA
## Datesenttocompany.year.fctr2015 NA
## Datesenttocompany.year.fctr2016 NA
## Response.fctrClosed -0.694
## Response.fctrClosedwithmonetaryrelief -6.201
## `Response.fctrClosedwithnon-monetaryrelief` -4.351
## Response.fctrInprogress NA
## Response.fctrUntimelyresponse NA
## `Rgn.Dvn.fctrMW#EastNorthCentral` -1.695
## `Rgn.Dvn.fctrMW#WestNorthCentral` -1.216
## `Rgn.Dvn.fctrMW#WestNorthCentral#MidAtlantic` -0.161
## `Rgn.Dvn.fctrMW#WestNorthCentral#NewEngland` -0.624
## `Rgn.Dvn.fctrOT#Other` 0.103
## `Rgn.Dvn.fctrSH#EastSouthCentral` -1.280
## `Rgn.Dvn.fctrSH#WestSouthCentral` -0.306
## `Rgn.Dvn.fctrWT#Mountain` 0.712
## `Rgn.Dvn.fctrWT#Pacific` 0.308
## Sent.Recd.Dys -0.533
## Sent.Recd.Dys.log1p -0.590
## Sent.Recd.Dys.nexp -0.618
## Sent.Recd.Dys.root2 0.542
## gCConsent.fctrConsentnotprovided -1.949
## gCConsent.fctrConsentprovided -2.011
## gCConsent.fctrOther -0.116
## gCompany.fctrAmex 2.343
## gCompany.fctrBlackhawkNetworkHoldingsInc. 1.354
## gCompany.fctrCitibank 0.459
## gCompany.fctrComerica 0.236
## `gCompany.fctrNetSpendCorporation,aTSYSCompany` -1.633
## gCompany.fctrOther 0.121
## `gCompany.fctrPayPalHoldings,Inc.` 1.062
## gCompany.fctrU.S.Bancorp -0.153
## gSPrd.fctrVehicleloan NA
## gSPrd.fctrCheckingaccount NA
## gSPrd.fctrConventionalfixedmortgage NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.)` NA
## gSPrd.fctrIdonotknow NA
## gSPrd.fctr NA
## gSPrd.fctrOthermortgage NA
## gSPrd.fctrCreditcard NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM)` NA
## `gSPrd.fctrOtherbankproduct/service` NA
## `gSPrd.fctrNon-federalstudentloan` NA
## gSPrd.fctrFHAmortgage NA
## `gSPrd.fctr(CD)Certificateofdeposit` NA
## gSPrd.fctrHomeequityloanorlineofcredit NA
## gSPrd.fctrVehiclelease NA
## gSPrd.fctrSavingsaccount NA
## gSPrd.fctrFederalstudentloan NA
## gSPrd.fctrMortgage NA
## gSPrd.fctrReversemortgage NA
## gSPrd.fctrAuto NA
## gSPrd.fctrInstallmentloan NA
## gSPrd.fctrMedical NA
## gSPrd.fctrVAmortgage NA
## `gSPrd.fctrDomestic(US)moneytransfer` NA
## gSPrd.fctrInternationalmoneytransfer NA
## gSPrd.fctrPersonallineofcredit NA
## gSPrd.fctrSecondmortgage NA
## gSPrd.fctrCashingacheckwithoutanaccount NA
## gSPrd.fctrPaydayloan NA
## gSPrd.fctrCheckcashing NA
## gSPrd.fctrMobilewallet 0.647
## gSPrd.fctrGovernmentbenefitpaymentcard 0.363
## gSPrd.fctrGiftormerchantcard 0.591
## gSPrd.fctrGeneralpurposecard -0.030
## gSPrd.fctrTitleloan NA
## gSPrd.fctrForeigncurrencyexchange NA
## gSPrd.fctrRefundanticipationcheck NA
## gSPrd.fctrDebtsettlement NA
## gSPrd.fctrPayrollcard -0.770
## gSPrd.fctrIDprepaidcard -0.092
## gSPrd.fctrCreditrepair NA
## gSPrd.fctrPawnloan NA
## gSPrd.fctrMoneyorder NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks` NA
## gSPrd.fctrFederalstudentloanservicing NA
## gTags.fctrOlderAmerican 1.536
## gTags.fctrOlderAmericanServicemember 0.757
## gTags.fctrServicemember 2.780
## `gSPrd.fctrOther:.clusterid.fctr2` 1.391
## `gSPrd.fctrVehicleloan:.clusterid.fctr2` NA
## `gSPrd.fctrCheckingaccount:.clusterid.fctr2` NA
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr2` NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr2` NA
## `gSPrd.fctrIdonotknow:.clusterid.fctr2` NA
## `gSPrd.fctr:.clusterid.fctr2` NA
## `gSPrd.fctrOthermortgage:.clusterid.fctr2` NA
## `gSPrd.fctrCreditcard:.clusterid.fctr2` NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr2` NA
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr2` NA
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr2` NA
## `gSPrd.fctrFHAmortgage:.clusterid.fctr2` NA
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr2` NA
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr2` NA
## `gSPrd.fctrVehiclelease:.clusterid.fctr2` NA
## `gSPrd.fctrSavingsaccount:.clusterid.fctr2` NA
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr2` NA
## `gSPrd.fctrMortgage:.clusterid.fctr2` NA
## `gSPrd.fctrReversemortgage:.clusterid.fctr2` NA
## `gSPrd.fctrAuto:.clusterid.fctr2` NA
## `gSPrd.fctrInstallmentloan:.clusterid.fctr2` NA
## `gSPrd.fctrMedical:.clusterid.fctr2` NA
## `gSPrd.fctrVAmortgage:.clusterid.fctr2` NA
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr2` NA
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr2` NA
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr2` NA
## `gSPrd.fctrSecondmortgage:.clusterid.fctr2` NA
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr2` NA
## `gSPrd.fctrPaydayloan:.clusterid.fctr2` NA
## `gSPrd.fctrCheckcashing:.clusterid.fctr2` NA
## `gSPrd.fctrMobilewallet:.clusterid.fctr2` 0.150
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr2` -0.264
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr2` -0.440
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr2` 1.214
## `gSPrd.fctrTitleloan:.clusterid.fctr2` NA
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr2` NA
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr2` NA
## `gSPrd.fctrDebtsettlement:.clusterid.fctr2` NA
## `gSPrd.fctrPayrollcard:.clusterid.fctr2` 1.660
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr2` -0.311
## `gSPrd.fctrCreditrepair:.clusterid.fctr2` NA
## `gSPrd.fctrPawnloan:.clusterid.fctr2` NA
## `gSPrd.fctrMoneyorder:.clusterid.fctr2` NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr2` NA
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr2` NA
## `gSPrd.fctrOther:.clusterid.fctr3` 0.492
## `gSPrd.fctrVehicleloan:.clusterid.fctr3` NA
## `gSPrd.fctrCheckingaccount:.clusterid.fctr3` NA
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr3` NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr3` NA
## `gSPrd.fctrIdonotknow:.clusterid.fctr3` NA
## `gSPrd.fctr:.clusterid.fctr3` NA
## `gSPrd.fctrOthermortgage:.clusterid.fctr3` NA
## `gSPrd.fctrCreditcard:.clusterid.fctr3` NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr3` NA
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr3` NA
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr3` NA
## `gSPrd.fctrFHAmortgage:.clusterid.fctr3` NA
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr3` NA
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr3` NA
## `gSPrd.fctrVehiclelease:.clusterid.fctr3` NA
## `gSPrd.fctrSavingsaccount:.clusterid.fctr3` NA
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr3` NA
## `gSPrd.fctrMortgage:.clusterid.fctr3` NA
## `gSPrd.fctrReversemortgage:.clusterid.fctr3` NA
## `gSPrd.fctrAuto:.clusterid.fctr3` NA
## `gSPrd.fctrInstallmentloan:.clusterid.fctr3` NA
## `gSPrd.fctrMedical:.clusterid.fctr3` NA
## `gSPrd.fctrVAmortgage:.clusterid.fctr3` NA
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr3` NA
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr3` NA
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr3` NA
## `gSPrd.fctrSecondmortgage:.clusterid.fctr3` NA
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr3` NA
## `gSPrd.fctrPaydayloan:.clusterid.fctr3` NA
## `gSPrd.fctrCheckcashing:.clusterid.fctr3` NA
## `gSPrd.fctrMobilewallet:.clusterid.fctr3` 2.215
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr3` NA
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr3` -0.215
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr3` -0.501
## `gSPrd.fctrTitleloan:.clusterid.fctr3` NA
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr3` NA
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr3` NA
## `gSPrd.fctrDebtsettlement:.clusterid.fctr3` NA
## `gSPrd.fctrPayrollcard:.clusterid.fctr3` NA
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr3` -0.272
## `gSPrd.fctrCreditrepair:.clusterid.fctr3` NA
## `gSPrd.fctrPawnloan:.clusterid.fctr3` NA
## `gSPrd.fctrMoneyorder:.clusterid.fctr3` NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr3` NA
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr3` NA
## `gSPrd.fctrOther:.clusterid.fctr4` -0.025
## `gSPrd.fctrVehicleloan:.clusterid.fctr4` NA
## `gSPrd.fctrCheckingaccount:.clusterid.fctr4` NA
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr4` NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr4` NA
## `gSPrd.fctrIdonotknow:.clusterid.fctr4` NA
## `gSPrd.fctr:.clusterid.fctr4` NA
## `gSPrd.fctrOthermortgage:.clusterid.fctr4` NA
## `gSPrd.fctrCreditcard:.clusterid.fctr4` NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr4` NA
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr4` NA
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr4` NA
## `gSPrd.fctrFHAmortgage:.clusterid.fctr4` NA
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr4` NA
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr4` NA
## `gSPrd.fctrVehiclelease:.clusterid.fctr4` NA
## `gSPrd.fctrSavingsaccount:.clusterid.fctr4` NA
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr4` NA
## `gSPrd.fctrMortgage:.clusterid.fctr4` NA
## `gSPrd.fctrReversemortgage:.clusterid.fctr4` NA
## `gSPrd.fctrAuto:.clusterid.fctr4` NA
## `gSPrd.fctrInstallmentloan:.clusterid.fctr4` NA
## `gSPrd.fctrMedical:.clusterid.fctr4` NA
## `gSPrd.fctrVAmortgage:.clusterid.fctr4` NA
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr4` NA
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr4` NA
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr4` NA
## `gSPrd.fctrSecondmortgage:.clusterid.fctr4` NA
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr4` NA
## `gSPrd.fctrPaydayloan:.clusterid.fctr4` NA
## `gSPrd.fctrCheckcashing:.clusterid.fctr4` NA
## `gSPrd.fctrMobilewallet:.clusterid.fctr4` NA
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr4` NA
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr4` NA
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr4` NA
## `gSPrd.fctrTitleloan:.clusterid.fctr4` NA
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr4` NA
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr4` NA
## `gSPrd.fctrDebtsettlement:.clusterid.fctr4` NA
## `gSPrd.fctrPayrollcard:.clusterid.fctr4` NA
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr4` NA
## `gSPrd.fctrCreditrepair:.clusterid.fctr4` NA
## `gSPrd.fctrPawnloan:.clusterid.fctr4` NA
## `gSPrd.fctrMoneyorder:.clusterid.fctr4` NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr4` NA
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr4` NA
## Pr(>|z|)
## (Intercept) 0.949739
## .pos 0.004032
## .rnorm 0.859924
## Channel.fctrFax 0.980633
## Channel.fctrPhone 7.01e-05
## Channel.fctrPostalmail 0.068296
## Channel.fctrReferral 0.000112
## ComplaintID 0.799550
## `Datereceived.date.fctr(7,13]` 0.834129
## `Datereceived.date.fctr(13,19]` 0.443137
## `Datereceived.date.fctr(19,25]` 0.527856
## `Datereceived.date.fctr(25,31]` 0.454826
## Datereceived.juliandate 0.599276
## Datereceived.last16.log1p 0.249325
## Datereceived.last2.log1p 0.000205
## Datereceived.last32.log1p 0.682196
## Datereceived.last4.log1p 0.369121
## Datereceived.last8.log1p 0.359309
## Datereceived.month.fctr02 0.593520
## Datereceived.month.fctr03 0.506735
## Datereceived.month.fctr04 0.605552
## Datereceived.month.fctr05 0.662896
## Datereceived.month.fctr06 0.502050
## Datereceived.month.fctr07 0.633808
## Datereceived.month.fctr08 0.615592
## Datereceived.month.fctr09 0.619857
## Datereceived.month.fctr10 0.620486
## Datereceived.month.fctr11 0.576974
## Datereceived.month.fctr12 0.610751
## Datereceived.wkday.fctr1 0.009567
## Datereceived.wkday.fctr2 0.045140
## Datereceived.wkday.fctr3 0.023121
## Datereceived.wkday.fctr4 0.008362
## Datereceived.wkday.fctr5 0.006902
## Datereceived.wkday.fctr6 0.187783
## Datereceived.wkend NA
## Datereceived.year.fctr2015 0.776375
## Datereceived.year.fctr2016 0.785088
## `Datesenttocompany.date.fctr(7,13]` NA
## `Datesenttocompany.date.fctr(13,19]` NA
## `Datesenttocompany.date.fctr(19,25]` NA
## `Datesenttocompany.date.fctr(25,31]` NA
## Datesenttocompany.juliandate NA
## Datesenttocompany.last16.log1p NA
## Datesenttocompany.last2.log1p NA
## Datesenttocompany.last32.log1p NA
## Datesenttocompany.last4.log1p NA
## Datesenttocompany.last8.log1p NA
## Datesenttocompany.month.fctr02 NA
## Datesenttocompany.month.fctr03 NA
## Datesenttocompany.month.fctr04 NA
## Datesenttocompany.month.fctr05 NA
## Datesenttocompany.month.fctr06 NA
## Datesenttocompany.month.fctr07 NA
## Datesenttocompany.month.fctr08 NA
## Datesenttocompany.month.fctr09 NA
## Datesenttocompany.month.fctr10 NA
## Datesenttocompany.month.fctr11 NA
## Datesenttocompany.month.fctr12 NA
## Datesenttocompany.wkday.fctr1 NA
## Datesenttocompany.wkday.fctr2 NA
## Datesenttocompany.wkday.fctr3 NA
## Datesenttocompany.wkday.fctr4 NA
## Datesenttocompany.wkday.fctr5 NA
## Datesenttocompany.wkday.fctr6 NA
## Datesenttocompany.wkend NA
## Datesenttocompany.year.fctr2015 NA
## Datesenttocompany.year.fctr2016 NA
## Response.fctrClosed 0.487643
## Response.fctrClosedwithmonetaryrelief 5.62e-10
## `Response.fctrClosedwithnon-monetaryrelief` 1.36e-05
## Response.fctrInprogress NA
## Response.fctrUntimelyresponse NA
## `Rgn.Dvn.fctrMW#EastNorthCentral` 0.090069
## `Rgn.Dvn.fctrMW#WestNorthCentral` 0.224156
## `Rgn.Dvn.fctrMW#WestNorthCentral#MidAtlantic` 0.872311
## `Rgn.Dvn.fctrMW#WestNorthCentral#NewEngland` 0.532651
## `Rgn.Dvn.fctrOT#Other` 0.917662
## `Rgn.Dvn.fctrSH#EastSouthCentral` 0.200661
## `Rgn.Dvn.fctrSH#WestSouthCentral` 0.759358
## `Rgn.Dvn.fctrWT#Mountain` 0.476273
## `Rgn.Dvn.fctrWT#Pacific` 0.757926
## Sent.Recd.Dys 0.593813
## Sent.Recd.Dys.log1p 0.555113
## Sent.Recd.Dys.nexp 0.536315
## Sent.Recd.Dys.root2 0.587927
## gCConsent.fctrConsentnotprovided 0.051347
## gCConsent.fctrConsentprovided 0.044335
## gCConsent.fctrOther 0.907838
## gCompany.fctrAmex 0.019152
## gCompany.fctrBlackhawkNetworkHoldingsInc. 0.175857
## gCompany.fctrCitibank 0.646544
## gCompany.fctrComerica 0.813688
## `gCompany.fctrNetSpendCorporation,aTSYSCompany` 0.102477
## gCompany.fctrOther 0.903961
## `gCompany.fctrPayPalHoldings,Inc.` 0.288182
## gCompany.fctrU.S.Bancorp 0.878128
## gSPrd.fctrVehicleloan NA
## gSPrd.fctrCheckingaccount NA
## gSPrd.fctrConventionalfixedmortgage NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.)` NA
## gSPrd.fctrIdonotknow NA
## gSPrd.fctr NA
## gSPrd.fctrOthermortgage NA
## gSPrd.fctrCreditcard NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM)` NA
## `gSPrd.fctrOtherbankproduct/service` NA
## `gSPrd.fctrNon-federalstudentloan` NA
## gSPrd.fctrFHAmortgage NA
## `gSPrd.fctr(CD)Certificateofdeposit` NA
## gSPrd.fctrHomeequityloanorlineofcredit NA
## gSPrd.fctrVehiclelease NA
## gSPrd.fctrSavingsaccount NA
## gSPrd.fctrFederalstudentloan NA
## gSPrd.fctrMortgage NA
## gSPrd.fctrReversemortgage NA
## gSPrd.fctrAuto NA
## gSPrd.fctrInstallmentloan NA
## gSPrd.fctrMedical NA
## gSPrd.fctrVAmortgage NA
## `gSPrd.fctrDomestic(US)moneytransfer` NA
## gSPrd.fctrInternationalmoneytransfer NA
## gSPrd.fctrPersonallineofcredit NA
## gSPrd.fctrSecondmortgage NA
## gSPrd.fctrCashingacheckwithoutanaccount NA
## gSPrd.fctrPaydayloan NA
## gSPrd.fctrCheckcashing NA
## gSPrd.fctrMobilewallet 0.517435
## gSPrd.fctrGovernmentbenefitpaymentcard 0.716775
## gSPrd.fctrGiftormerchantcard 0.554605
## gSPrd.fctrGeneralpurposecard 0.976162
## gSPrd.fctrTitleloan NA
## gSPrd.fctrForeigncurrencyexchange NA
## gSPrd.fctrRefundanticipationcheck NA
## gSPrd.fctrDebtsettlement NA
## gSPrd.fctrPayrollcard 0.441031
## gSPrd.fctrIDprepaidcard 0.926894
## gSPrd.fctrCreditrepair NA
## gSPrd.fctrPawnloan NA
## gSPrd.fctrMoneyorder NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks` NA
## gSPrd.fctrFederalstudentloanservicing NA
## gTags.fctrOlderAmerican 0.124564
## gTags.fctrOlderAmericanServicemember 0.448989
## gTags.fctrServicemember 0.005439
## `gSPrd.fctrOther:.clusterid.fctr2` 0.164148
## `gSPrd.fctrVehicleloan:.clusterid.fctr2` NA
## `gSPrd.fctrCheckingaccount:.clusterid.fctr2` NA
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr2` NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr2` NA
## `gSPrd.fctrIdonotknow:.clusterid.fctr2` NA
## `gSPrd.fctr:.clusterid.fctr2` NA
## `gSPrd.fctrOthermortgage:.clusterid.fctr2` NA
## `gSPrd.fctrCreditcard:.clusterid.fctr2` NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr2` NA
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr2` NA
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr2` NA
## `gSPrd.fctrFHAmortgage:.clusterid.fctr2` NA
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr2` NA
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr2` NA
## `gSPrd.fctrVehiclelease:.clusterid.fctr2` NA
## `gSPrd.fctrSavingsaccount:.clusterid.fctr2` NA
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr2` NA
## `gSPrd.fctrMortgage:.clusterid.fctr2` NA
## `gSPrd.fctrReversemortgage:.clusterid.fctr2` NA
## `gSPrd.fctrAuto:.clusterid.fctr2` NA
## `gSPrd.fctrInstallmentloan:.clusterid.fctr2` NA
## `gSPrd.fctrMedical:.clusterid.fctr2` NA
## `gSPrd.fctrVAmortgage:.clusterid.fctr2` NA
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr2` NA
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr2` NA
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr2` NA
## `gSPrd.fctrSecondmortgage:.clusterid.fctr2` NA
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr2` NA
## `gSPrd.fctrPaydayloan:.clusterid.fctr2` NA
## `gSPrd.fctrCheckcashing:.clusterid.fctr2` NA
## `gSPrd.fctrMobilewallet:.clusterid.fctr2` 0.880378
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr2` 0.791401
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr2` 0.660101
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr2` 0.224729
## `gSPrd.fctrTitleloan:.clusterid.fctr2` NA
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr2` NA
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr2` NA
## `gSPrd.fctrDebtsettlement:.clusterid.fctr2` NA
## `gSPrd.fctrPayrollcard:.clusterid.fctr2` 0.096862
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr2` 0.756074
## `gSPrd.fctrCreditrepair:.clusterid.fctr2` NA
## `gSPrd.fctrPawnloan:.clusterid.fctr2` NA
## `gSPrd.fctrMoneyorder:.clusterid.fctr2` NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr2` NA
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr2` NA
## `gSPrd.fctrOther:.clusterid.fctr3` 0.623054
## `gSPrd.fctrVehicleloan:.clusterid.fctr3` NA
## `gSPrd.fctrCheckingaccount:.clusterid.fctr3` NA
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr3` NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr3` NA
## `gSPrd.fctrIdonotknow:.clusterid.fctr3` NA
## `gSPrd.fctr:.clusterid.fctr3` NA
## `gSPrd.fctrOthermortgage:.clusterid.fctr3` NA
## `gSPrd.fctrCreditcard:.clusterid.fctr3` NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr3` NA
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr3` NA
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr3` NA
## `gSPrd.fctrFHAmortgage:.clusterid.fctr3` NA
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr3` NA
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr3` NA
## `gSPrd.fctrVehiclelease:.clusterid.fctr3` NA
## `gSPrd.fctrSavingsaccount:.clusterid.fctr3` NA
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr3` NA
## `gSPrd.fctrMortgage:.clusterid.fctr3` NA
## `gSPrd.fctrReversemortgage:.clusterid.fctr3` NA
## `gSPrd.fctrAuto:.clusterid.fctr3` NA
## `gSPrd.fctrInstallmentloan:.clusterid.fctr3` NA
## `gSPrd.fctrMedical:.clusterid.fctr3` NA
## `gSPrd.fctrVAmortgage:.clusterid.fctr3` NA
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr3` NA
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr3` NA
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr3` NA
## `gSPrd.fctrSecondmortgage:.clusterid.fctr3` NA
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr3` NA
## `gSPrd.fctrPaydayloan:.clusterid.fctr3` NA
## `gSPrd.fctrCheckcashing:.clusterid.fctr3` NA
## `gSPrd.fctrMobilewallet:.clusterid.fctr3` 0.026748
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr3` NA
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr3` 0.829830
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr3` 0.616486
## `gSPrd.fctrTitleloan:.clusterid.fctr3` NA
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr3` NA
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr3` NA
## `gSPrd.fctrDebtsettlement:.clusterid.fctr3` NA
## `gSPrd.fctrPayrollcard:.clusterid.fctr3` NA
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr3` 0.785623
## `gSPrd.fctrCreditrepair:.clusterid.fctr3` NA
## `gSPrd.fctrPawnloan:.clusterid.fctr3` NA
## `gSPrd.fctrMoneyorder:.clusterid.fctr3` NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr3` NA
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr3` NA
## `gSPrd.fctrOther:.clusterid.fctr4` 0.979681
## `gSPrd.fctrVehicleloan:.clusterid.fctr4` NA
## `gSPrd.fctrCheckingaccount:.clusterid.fctr4` NA
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr4` NA
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr4` NA
## `gSPrd.fctrIdonotknow:.clusterid.fctr4` NA
## `gSPrd.fctr:.clusterid.fctr4` NA
## `gSPrd.fctrOthermortgage:.clusterid.fctr4` NA
## `gSPrd.fctrCreditcard:.clusterid.fctr4` NA
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr4` NA
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr4` NA
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr4` NA
## `gSPrd.fctrFHAmortgage:.clusterid.fctr4` NA
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr4` NA
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr4` NA
## `gSPrd.fctrVehiclelease:.clusterid.fctr4` NA
## `gSPrd.fctrSavingsaccount:.clusterid.fctr4` NA
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr4` NA
## `gSPrd.fctrMortgage:.clusterid.fctr4` NA
## `gSPrd.fctrReversemortgage:.clusterid.fctr4` NA
## `gSPrd.fctrAuto:.clusterid.fctr4` NA
## `gSPrd.fctrInstallmentloan:.clusterid.fctr4` NA
## `gSPrd.fctrMedical:.clusterid.fctr4` NA
## `gSPrd.fctrVAmortgage:.clusterid.fctr4` NA
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr4` NA
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr4` NA
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr4` NA
## `gSPrd.fctrSecondmortgage:.clusterid.fctr4` NA
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr4` NA
## `gSPrd.fctrPaydayloan:.clusterid.fctr4` NA
## `gSPrd.fctrCheckcashing:.clusterid.fctr4` NA
## `gSPrd.fctrMobilewallet:.clusterid.fctr4` NA
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr4` NA
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr4` NA
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr4` NA
## `gSPrd.fctrTitleloan:.clusterid.fctr4` NA
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr4` NA
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr4` NA
## `gSPrd.fctrDebtsettlement:.clusterid.fctr4` NA
## `gSPrd.fctrPayrollcard:.clusterid.fctr4` NA
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr4` NA
## `gSPrd.fctrCreditrepair:.clusterid.fctr4` NA
## `gSPrd.fctrPawnloan:.clusterid.fctr4` NA
## `gSPrd.fctrMoneyorder:.clusterid.fctr4` NA
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr4` NA
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr4` NA
##
## (Intercept)
## .pos **
## .rnorm
## Channel.fctrFax
## Channel.fctrPhone ***
## Channel.fctrPostalmail .
## Channel.fctrReferral ***
## ComplaintID
## `Datereceived.date.fctr(7,13]`
## `Datereceived.date.fctr(13,19]`
## `Datereceived.date.fctr(19,25]`
## `Datereceived.date.fctr(25,31]`
## Datereceived.juliandate
## Datereceived.last16.log1p
## Datereceived.last2.log1p ***
## Datereceived.last32.log1p
## Datereceived.last4.log1p
## Datereceived.last8.log1p
## Datereceived.month.fctr02
## Datereceived.month.fctr03
## Datereceived.month.fctr04
## Datereceived.month.fctr05
## Datereceived.month.fctr06
## Datereceived.month.fctr07
## Datereceived.month.fctr08
## Datereceived.month.fctr09
## Datereceived.month.fctr10
## Datereceived.month.fctr11
## Datereceived.month.fctr12
## Datereceived.wkday.fctr1 **
## Datereceived.wkday.fctr2 *
## Datereceived.wkday.fctr3 *
## Datereceived.wkday.fctr4 **
## Datereceived.wkday.fctr5 **
## Datereceived.wkday.fctr6
## Datereceived.wkend
## Datereceived.year.fctr2015
## Datereceived.year.fctr2016
## `Datesenttocompany.date.fctr(7,13]`
## `Datesenttocompany.date.fctr(13,19]`
## `Datesenttocompany.date.fctr(19,25]`
## `Datesenttocompany.date.fctr(25,31]`
## Datesenttocompany.juliandate
## Datesenttocompany.last16.log1p
## Datesenttocompany.last2.log1p
## Datesenttocompany.last32.log1p
## Datesenttocompany.last4.log1p
## Datesenttocompany.last8.log1p
## Datesenttocompany.month.fctr02
## Datesenttocompany.month.fctr03
## Datesenttocompany.month.fctr04
## Datesenttocompany.month.fctr05
## Datesenttocompany.month.fctr06
## Datesenttocompany.month.fctr07
## Datesenttocompany.month.fctr08
## Datesenttocompany.month.fctr09
## Datesenttocompany.month.fctr10
## Datesenttocompany.month.fctr11
## Datesenttocompany.month.fctr12
## Datesenttocompany.wkday.fctr1
## Datesenttocompany.wkday.fctr2
## Datesenttocompany.wkday.fctr3
## Datesenttocompany.wkday.fctr4
## Datesenttocompany.wkday.fctr5
## Datesenttocompany.wkday.fctr6
## Datesenttocompany.wkend
## Datesenttocompany.year.fctr2015
## Datesenttocompany.year.fctr2016
## Response.fctrClosed
## Response.fctrClosedwithmonetaryrelief ***
## `Response.fctrClosedwithnon-monetaryrelief` ***
## Response.fctrInprogress
## Response.fctrUntimelyresponse
## `Rgn.Dvn.fctrMW#EastNorthCentral` .
## `Rgn.Dvn.fctrMW#WestNorthCentral`
## `Rgn.Dvn.fctrMW#WestNorthCentral#MidAtlantic`
## `Rgn.Dvn.fctrMW#WestNorthCentral#NewEngland`
## `Rgn.Dvn.fctrOT#Other`
## `Rgn.Dvn.fctrSH#EastSouthCentral`
## `Rgn.Dvn.fctrSH#WestSouthCentral`
## `Rgn.Dvn.fctrWT#Mountain`
## `Rgn.Dvn.fctrWT#Pacific`
## Sent.Recd.Dys
## Sent.Recd.Dys.log1p
## Sent.Recd.Dys.nexp
## Sent.Recd.Dys.root2
## gCConsent.fctrConsentnotprovided .
## gCConsent.fctrConsentprovided *
## gCConsent.fctrOther
## gCompany.fctrAmex *
## gCompany.fctrBlackhawkNetworkHoldingsInc.
## gCompany.fctrCitibank
## gCompany.fctrComerica
## `gCompany.fctrNetSpendCorporation,aTSYSCompany`
## gCompany.fctrOther
## `gCompany.fctrPayPalHoldings,Inc.`
## gCompany.fctrU.S.Bancorp
## gSPrd.fctrVehicleloan
## gSPrd.fctrCheckingaccount
## gSPrd.fctrConventionalfixedmortgage
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.)`
## gSPrd.fctrIdonotknow
## gSPrd.fctr
## gSPrd.fctrOthermortgage
## gSPrd.fctrCreditcard
## `gSPrd.fctrConventionaladjustablemortgage(ARM)`
## `gSPrd.fctrOtherbankproduct/service`
## `gSPrd.fctrNon-federalstudentloan`
## gSPrd.fctrFHAmortgage
## `gSPrd.fctr(CD)Certificateofdeposit`
## gSPrd.fctrHomeequityloanorlineofcredit
## gSPrd.fctrVehiclelease
## gSPrd.fctrSavingsaccount
## gSPrd.fctrFederalstudentloan
## gSPrd.fctrMortgage
## gSPrd.fctrReversemortgage
## gSPrd.fctrAuto
## gSPrd.fctrInstallmentloan
## gSPrd.fctrMedical
## gSPrd.fctrVAmortgage
## `gSPrd.fctrDomestic(US)moneytransfer`
## gSPrd.fctrInternationalmoneytransfer
## gSPrd.fctrPersonallineofcredit
## gSPrd.fctrSecondmortgage
## gSPrd.fctrCashingacheckwithoutanaccount
## gSPrd.fctrPaydayloan
## gSPrd.fctrCheckcashing
## gSPrd.fctrMobilewallet
## gSPrd.fctrGovernmentbenefitpaymentcard
## gSPrd.fctrGiftormerchantcard
## gSPrd.fctrGeneralpurposecard
## gSPrd.fctrTitleloan
## gSPrd.fctrForeigncurrencyexchange
## gSPrd.fctrRefundanticipationcheck
## gSPrd.fctrDebtsettlement
## gSPrd.fctrPayrollcard
## gSPrd.fctrIDprepaidcard
## gSPrd.fctrCreditrepair
## gSPrd.fctrPawnloan
## gSPrd.fctrMoneyorder
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks`
## gSPrd.fctrFederalstudentloanservicing
## gTags.fctrOlderAmerican
## gTags.fctrOlderAmericanServicemember
## gTags.fctrServicemember **
## `gSPrd.fctrOther:.clusterid.fctr2`
## `gSPrd.fctrVehicleloan:.clusterid.fctr2`
## `gSPrd.fctrCheckingaccount:.clusterid.fctr2`
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr2`
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr2`
## `gSPrd.fctrIdonotknow:.clusterid.fctr2`
## `gSPrd.fctr:.clusterid.fctr2`
## `gSPrd.fctrOthermortgage:.clusterid.fctr2`
## `gSPrd.fctrCreditcard:.clusterid.fctr2`
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr2`
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr2`
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr2`
## `gSPrd.fctrFHAmortgage:.clusterid.fctr2`
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr2`
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr2`
## `gSPrd.fctrVehiclelease:.clusterid.fctr2`
## `gSPrd.fctrSavingsaccount:.clusterid.fctr2`
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr2`
## `gSPrd.fctrMortgage:.clusterid.fctr2`
## `gSPrd.fctrReversemortgage:.clusterid.fctr2`
## `gSPrd.fctrAuto:.clusterid.fctr2`
## `gSPrd.fctrInstallmentloan:.clusterid.fctr2`
## `gSPrd.fctrMedical:.clusterid.fctr2`
## `gSPrd.fctrVAmortgage:.clusterid.fctr2`
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr2`
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr2`
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr2`
## `gSPrd.fctrSecondmortgage:.clusterid.fctr2`
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr2`
## `gSPrd.fctrPaydayloan:.clusterid.fctr2`
## `gSPrd.fctrCheckcashing:.clusterid.fctr2`
## `gSPrd.fctrMobilewallet:.clusterid.fctr2`
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr2`
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr2`
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr2`
## `gSPrd.fctrTitleloan:.clusterid.fctr2`
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr2`
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr2`
## `gSPrd.fctrDebtsettlement:.clusterid.fctr2`
## `gSPrd.fctrPayrollcard:.clusterid.fctr2` .
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr2`
## `gSPrd.fctrCreditrepair:.clusterid.fctr2`
## `gSPrd.fctrPawnloan:.clusterid.fctr2`
## `gSPrd.fctrMoneyorder:.clusterid.fctr2`
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr2`
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr2`
## `gSPrd.fctrOther:.clusterid.fctr3`
## `gSPrd.fctrVehicleloan:.clusterid.fctr3`
## `gSPrd.fctrCheckingaccount:.clusterid.fctr3`
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr3`
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr3`
## `gSPrd.fctrIdonotknow:.clusterid.fctr3`
## `gSPrd.fctr:.clusterid.fctr3`
## `gSPrd.fctrOthermortgage:.clusterid.fctr3`
## `gSPrd.fctrCreditcard:.clusterid.fctr3`
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr3`
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr3`
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr3`
## `gSPrd.fctrFHAmortgage:.clusterid.fctr3`
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr3`
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr3`
## `gSPrd.fctrVehiclelease:.clusterid.fctr3`
## `gSPrd.fctrSavingsaccount:.clusterid.fctr3`
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr3`
## `gSPrd.fctrMortgage:.clusterid.fctr3`
## `gSPrd.fctrReversemortgage:.clusterid.fctr3`
## `gSPrd.fctrAuto:.clusterid.fctr3`
## `gSPrd.fctrInstallmentloan:.clusterid.fctr3`
## `gSPrd.fctrMedical:.clusterid.fctr3`
## `gSPrd.fctrVAmortgage:.clusterid.fctr3`
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr3`
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr3`
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr3`
## `gSPrd.fctrSecondmortgage:.clusterid.fctr3`
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr3`
## `gSPrd.fctrPaydayloan:.clusterid.fctr3`
## `gSPrd.fctrCheckcashing:.clusterid.fctr3`
## `gSPrd.fctrMobilewallet:.clusterid.fctr3` *
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr3`
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr3`
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr3`
## `gSPrd.fctrTitleloan:.clusterid.fctr3`
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr3`
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr3`
## `gSPrd.fctrDebtsettlement:.clusterid.fctr3`
## `gSPrd.fctrPayrollcard:.clusterid.fctr3`
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr3`
## `gSPrd.fctrCreditrepair:.clusterid.fctr3`
## `gSPrd.fctrPawnloan:.clusterid.fctr3`
## `gSPrd.fctrMoneyorder:.clusterid.fctr3`
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr3`
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr3`
## `gSPrd.fctrOther:.clusterid.fctr4`
## `gSPrd.fctrVehicleloan:.clusterid.fctr4`
## `gSPrd.fctrCheckingaccount:.clusterid.fctr4`
## `gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr4`
## `gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr4`
## `gSPrd.fctrIdonotknow:.clusterid.fctr4`
## `gSPrd.fctr:.clusterid.fctr4`
## `gSPrd.fctrOthermortgage:.clusterid.fctr4`
## `gSPrd.fctrCreditcard:.clusterid.fctr4`
## `gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr4`
## `gSPrd.fctrOtherbankproduct/service:.clusterid.fctr4`
## `gSPrd.fctrNon-federalstudentloan:.clusterid.fctr4`
## `gSPrd.fctrFHAmortgage:.clusterid.fctr4`
## `gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr4`
## `gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr4`
## `gSPrd.fctrVehiclelease:.clusterid.fctr4`
## `gSPrd.fctrSavingsaccount:.clusterid.fctr4`
## `gSPrd.fctrFederalstudentloan:.clusterid.fctr4`
## `gSPrd.fctrMortgage:.clusterid.fctr4`
## `gSPrd.fctrReversemortgage:.clusterid.fctr4`
## `gSPrd.fctrAuto:.clusterid.fctr4`
## `gSPrd.fctrInstallmentloan:.clusterid.fctr4`
## `gSPrd.fctrMedical:.clusterid.fctr4`
## `gSPrd.fctrVAmortgage:.clusterid.fctr4`
## `gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr4`
## `gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr4`
## `gSPrd.fctrPersonallineofcredit:.clusterid.fctr4`
## `gSPrd.fctrSecondmortgage:.clusterid.fctr4`
## `gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr4`
## `gSPrd.fctrPaydayloan:.clusterid.fctr4`
## `gSPrd.fctrCheckcashing:.clusterid.fctr4`
## `gSPrd.fctrMobilewallet:.clusterid.fctr4`
## `gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr4`
## `gSPrd.fctrGiftormerchantcard:.clusterid.fctr4`
## `gSPrd.fctrGeneralpurposecard:.clusterid.fctr4`
## `gSPrd.fctrTitleloan:.clusterid.fctr4`
## `gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr4`
## `gSPrd.fctrRefundanticipationcheck:.clusterid.fctr4`
## `gSPrd.fctrDebtsettlement:.clusterid.fctr4`
## `gSPrd.fctrPayrollcard:.clusterid.fctr4`
## `gSPrd.fctrIDprepaidcard:.clusterid.fctr4`
## `gSPrd.fctrCreditrepair:.clusterid.fctr4`
## `gSPrd.fctrPawnloan:.clusterid.fctr4`
## `gSPrd.fctrMoneyorder:.clusterid.fctr4`
## `gSPrd.fctrTraveler\\342\\200\\231s/Cashier\\342\\200\\231schecks:.clusterid.fctr4`
## `gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr4`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1966.6 on 2293 degrees of freedom
## Residual deviance: 1623.2 on 2208 degrees of freedom
## AIC: 1795.2
##
## Number of Fisher Scoring iterations: 15
##
## [1] "mydisplayOutliers: "
##
## No Studentized residuals with Bonferonni p < 0.05
## Largest |rstudent|:
## rstudent unadjusted p-value Bonferonni p
## X84024 3.183437 0.0014554 NA
## [1] ""
## .rstudent .dffits .hatvalues
## Min. :-1.7531 Min. :-1.71957 Min. :0.002713
## 1st Qu.:-0.6082 1st Qu.:-0.15132 1st Qu.:0.015759
## Median :-0.3629 Median :-0.06350 Median :0.031726
## Mean :-0.1481 Mean :-0.03285 Mean :0.037489
## 3rd Qu.:-0.2061 3rd Qu.:-0.02342 3rd Qu.:0.050504
## Max. : 3.1834 Max. : 0.85272 Max. :0.808101
## [1] "myfit_mdl: train diagnostics complete: 49.223000 secs"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Prediction
## Reference N Y
## N 1895 47
## Y 282 70
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.565824e-01 2.403499e-01 8.415666e-01 8.706810e-01 8.465562e-01
## AccuracyPValue McnemarPValue
## 9.538193e-02 4.452313e-38## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Prediction
## Reference N Y
## N 488 2
## Y 83 2
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.521739e-01 3.208238e-02 8.204899e-01 8.801833e-01 8.521739e-01
## AccuracyPValue McnemarPValue
## 5.288965e-01 4.055661e-18
## [1] "myfit_mdl: predict complete: 55.891000 secs"
## id
## 1 All.X##rcv#glm
## feats
## 1 Datereceived.last8.log1p,Datesenttocompany.last8.log1p,Datereceived.last4.log1p,Datesenttocompany.last4.log1p,Datereceived.last2.log1p,Datesenttocompany.last2.log1p,Datereceived.last16.log1p,Datesenttocompany.last16.log1p,Datereceived.last32.log1p,Datesenttocompany.last32.log1p,.pos,gCompany.fctr,gTags.fctr,Sent.Recd.Dys.nexp,Rgn.Dvn.fctr,Datereceived.year.fctr,Datesenttocompany.year.fctr,ComplaintID,Datereceived.wkday.fctr,Datesenttocompany.wkday.fctr,gCConsent.fctr,.rnorm,Datereceived.wkend,Datesenttocompany.wkend,Channel.fctr,gSPrd.fctr,Datereceived.month.fctr,Datesenttocompany.month.fctr,Datereceived.juliandate,Datesenttocompany.juliandate,Datereceived.date.fctr,Datesenttocompany.date.fctr,Sent.Recd.Dys,Sent.Recd.Dys.root2,Sent.Recd.Dys.log1p,Response.fctr,gSPrd.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 47.994 4.141
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5558059 0.9866117 0.125 0.7879193
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.45 0.2985075 0.8453978
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8415666 0.870681 0.1337742
## min.log.loss.mean.fit max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## 1 0.3537964 0.5179472 0.9653061 0.07058824
## max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7127491 0.65 0.04494382 0.8521739
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8204899 0.8801833 0.03208238
## min.log.loss.mean.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.3952978 0.007957836 0.0533379## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## [1] "myfit_mdl: exit: 56.272000 secs"## label step_major step_minor label_minor bgn end
## 4 fit.models_1_All.X 1 3 glm 92.724 149.016
## 5 fit.models_1_preProc 1 4 preProc 149.017 NA
## elapsed
## 4 56.292
## 5 NA## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## The following object is masked from 'package:base':
##
## startsWith## max.Accuracy.OOB max.AUCROCR.OOB
## Low.cor.X##rcv#glmnet 0.8539130 0.7136134
## All.X##rcv#glmnet 0.8539130 0.7134214
## All.X##rcv#glm 0.8521739 0.7127491
## Max.cor.Y.Time.Lag##rcv#glmnet 0.8521739 0.6766867
## Max.cor.Y.rcv.1X1###glmnet 0.8521739 0.6620888
## MFO###myMFO_classfr 0.8521739 0.5000000
## Max.cor.Y##rcv#rpart 0.8521739 0.5000000
## Random###myrandom_classfr 0.8521739 0.4994598
## max.AUCpROC.OOB min.elapsedtime.everything
## Low.cor.X##rcv#glmnet 0.5156062 13.573
## All.X##rcv#glmnet 0.5145858 18.452
## All.X##rcv#glm 0.5179472 47.994
## Max.cor.Y.Time.Lag##rcv#glmnet 0.5000000 2.615
## Max.cor.Y.rcv.1X1###glmnet 0.5000000 0.545
## MFO###myMFO_classfr 0.5000000 0.209
## Max.cor.Y##rcv#rpart 0.5000000 1.518
## Random###myrandom_classfr 0.4971188 0.186
## max.Accuracy.fit
## Low.cor.X##rcv#glmnet 0.8484475
## All.X##rcv#glmnet 0.8481571
## All.X##rcv#glm 0.8453978
## Max.cor.Y.Time.Lag##rcv#glmnet 0.8465570
## Max.cor.Y.rcv.1X1###glmnet 0.8465562
## MFO###myMFO_classfr 0.8465562
## Max.cor.Y##rcv#rpart 0.8465570
## Random###myrandom_classfr 0.8465562## label step_major step_minor label_minor bgn end
## 5 fit.models_1_preProc 1 4 preProc 149.017 149.412
## 6 fit.models_1_end 1 5 teardown 149.412 NA
## elapsed
## 5 0.395
## 6 NA## label step_major step_minor label_minor bgn end elapsed
## 3 fit.models 2 1 1 63.628 149.418 85.79
## 4 fit.models 2 2 2 149.418 NA NA## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 150.884 NA NA## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"## Loading required package: RColorBrewer## Warning: Removed 5 rows containing missing values (geom_errorbar).## quartz_off_screen
## 2## Warning: Removed 5 rows containing missing values (geom_errorbar).
## id max.Accuracy.OOB max.AUCROCR.OOB
## 6 Low.cor.X##rcv#glmnet 0.8539130 0.7136134
## 7 All.X##rcv#glmnet 0.8539130 0.7134214
## 8 All.X##rcv#glm 0.8521739 0.7127491
## 5 Max.cor.Y.Time.Lag##rcv#glmnet 0.8521739 0.6766867
## 3 Max.cor.Y.rcv.1X1###glmnet 0.8521739 0.6620888
## 1 MFO###myMFO_classfr 0.8521739 0.5000000
## 4 Max.cor.Y##rcv#rpart 0.8521739 0.5000000
## 2 Random###myrandom_classfr 0.8521739 0.4994598
## max.AUCpROC.OOB min.elapsedtime.everything max.Accuracy.fit
## 6 0.5156062 13.573 0.8484475
## 7 0.5145858 18.452 0.8481571
## 8 0.5179472 47.994 0.8453978
## 5 0.5000000 2.615 0.8465570
## 3 0.5000000 0.545 0.8465562
## 1 0.5000000 0.209 0.8465562
## 4 0.5000000 1.518 0.8465570
## 2 0.4971188 0.186 0.8465562
## opt.prob.threshold.fit opt.prob.threshold.OOB
## 6 0.40 0.50
## 7 0.40 0.60
## 8 0.45 0.65
## 5 0.50 0.50
## 3 0.50 0.50
## 1 0.50 0.50
## 4 0.50 0.50
## 2 0.85 0.85## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB + min.elapsedtime.everything -
## max.Accuracy.fit - opt.prob.threshold.OOB
## <environment: 0x7fcff27bc2e0>## [1] "Best model id: Low.cor.X##rcv#glmnet"## glmnet
##
## 2294 samples
## 26 predictor
## 2 classes: 'N', 'Y'
##
## No pre-processing
## Resampling: Cross-Validated (7 fold, repeated 3 times)
## Summary of sample sizes: 1967, 1966, 1966, 1967, 1966, 1966, ...
## Resampling results across tuning parameters:
##
## alpha lambda Accuracy Kappa
## 0.100 5.728268e-05 0.8459785 0.13357496
## 0.100 2.658826e-04 0.8459785 0.13357496
## 0.100 1.234118e-03 0.8461224 0.12664715
## 0.100 5.728268e-03 0.8481598 0.11100660
## 0.100 2.658826e-02 0.8475751 0.04063488
## 0.325 5.728268e-05 0.8458333 0.13216412
## 0.325 2.658826e-04 0.8458333 0.13302600
## 0.325 1.234118e-03 0.8464136 0.12306822
## 0.325 5.728268e-03 0.8478667 0.08622118
## 0.325 2.658826e-02 0.8474290 0.01074232
## 0.550 5.728268e-05 0.8459785 0.13341853
## 0.550 2.658826e-04 0.8461241 0.13372555
## 0.550 1.234118e-03 0.8467053 0.11985825
## 0.550 5.728268e-03 0.8478659 0.07107953
## 0.550 2.658826e-02 0.8465570 0.00000000
## 0.775 5.728268e-05 0.8459785 0.13341853
## 0.775 2.658826e-04 0.8467049 0.13498515
## 0.775 1.234118e-03 0.8475782 0.11779355
## 0.775 5.728268e-03 0.8484475 0.06099040
## 0.775 2.658826e-02 0.8465570 0.00000000
## 1.000 5.728268e-05 0.8459785 0.13341853
## 1.000 2.658826e-04 0.8468500 0.13428349
## 1.000 1.234118e-03 0.8472869 0.11098345
## 1.000 5.728268e-03 0.8478663 0.04949103
## 1.000 2.658826e-02 0.8465570 0.00000000
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were alpha = 0.775 and lambda
## = 0.005728268.## [1] "Low.cor.X##rcv#glmnet fit prediction diagnostics:"## [1] "Low.cor.X##rcv#glmnet OOB prediction diagnostics:"## Low.cor.X..rcv.glmnet.imp
## Channel.fctrFax 1.000000e+02
## Response.fctrClosedwithnon-monetaryrelief 8.329310e+01
## Response.fctrClosedwithmonetaryrelief 7.885785e+01
## gSPrd.fctrMobilewallet:.clusterid.fctr3 6.997018e+01
## Datesenttocompany.month.fctr06 5.495954e+01
## gCompany.fctrAmex 5.284365e+01
## Channel.fctrPhone 4.598173e+01
## gCompany.fctrNetSpendCorporation,aTSYSCompany 4.563519e+01
## gTags.fctrServicemember 4.196423e+01
## gSPrd.fctrOther:.clusterid.fctr2 3.722203e+01
## Channel.fctrReferral 3.674772e+01
## gCompany.fctrBlackhawkNetworkHoldingsInc. 2.816743e+01
## Datesenttocompany.wkend 2.720754e+01
## gCConsent.fctrOther 2.442674e+01
## gTags.fctrOlderAmerican 2.315353e+01
## gSPrd.fctrMobilewallet 2.006931e+01
## Datesenttocompany.month.fctr05 1.985987e+01
## Datesenttocompany.month.fctr11 1.943992e+01
## Rgn.Dvn.fctrMW#EastNorthCentral 1.855754e+01
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3 1.813850e+01
## Datesenttocompany.month.fctr03 1.724040e+01
## Datesenttocompany.month.fctr12 1.706922e+01
## Datesenttocompany.month.fctr07 1.674624e+01
## Response.fctrClosed 1.642855e+01
## Rgn.Dvn.fctrMW#WestNorthCentral 1.537673e+01
## Rgn.Dvn.fctrSH#EastSouthCentral 1.503102e+01
## Rgn.Dvn.fctrWT#Mountain 1.255275e+01
## gSPrd.fctrPayrollcard 1.148085e+01
## Datesenttocompany.month.fctr09 9.426734e+00
## gCompany.fctrPayPalHoldings,Inc. 8.435248e+00
## Rgn.Dvn.fctrWT#Pacific 6.979498e+00
## gTags.fctrOlderAmericanServicemember 6.186618e+00
## Datesenttocompany.date.fctr(7,13] 5.803873e+00
## Sent.Recd.Dys.root2 4.348095e+00
## Datesenttocompany.last2.log1p 4.301632e+00
## Channel.fctrPostalmail 3.425176e+00
## gCompany.fctrCitibank 3.243715e+00
## gSPrd.fctrGiftormerchantcard 2.699154e+00
## Datesenttocompany.date.fctr(25,31] 2.697157e+00
## Datereceived.wkday.fctr2 2.453702e+00
## Datesenttocompany.last8.log1p 2.371454e+00
## Datesenttocompany.last4.log1p 1.345608e+00
## Datereceived.wkday.fctr4 5.206375e-01
## Datesenttocompany.last16.log1p 4.760548e-01
## Datesenttocompany.wkday.fctr2 3.873043e-01
## gSPrd.fctrIDprepaidcard:.clusterid.fctr2 7.205122e-02
## Sent.Recd.Dys 6.766756e-03
## Datesenttocompany.wkday.fctr4 3.993891e-04
## .pos 1.118247e-04
## ComplaintID 2.394310e-06
## .rnorm 0.000000e+00
## Datereceived.wkday.fctr1 0.000000e+00
## Datereceived.wkday.fctr3 0.000000e+00
## Datereceived.wkday.fctr5 0.000000e+00
## Datereceived.wkday.fctr6 0.000000e+00
## Datesenttocompany.date.fctr(13,19] 0.000000e+00
## Datesenttocompany.date.fctr(19,25] 0.000000e+00
## Datesenttocompany.juliandate 0.000000e+00
## Datesenttocompany.last32.log1p 0.000000e+00
## Datesenttocompany.month.fctr02 0.000000e+00
## Datesenttocompany.month.fctr04 0.000000e+00
## Datesenttocompany.month.fctr08 0.000000e+00
## Datesenttocompany.month.fctr10 0.000000e+00
## Datesenttocompany.wkday.fctr1 0.000000e+00
## Datesenttocompany.wkday.fctr3 0.000000e+00
## Datesenttocompany.wkday.fctr5 0.000000e+00
## Datesenttocompany.wkday.fctr6 0.000000e+00
## Datesenttocompany.year.fctr2015 0.000000e+00
## Datesenttocompany.year.fctr2016 0.000000e+00
## Response.fctrInprogress 0.000000e+00
## Response.fctrUntimelyresponse 0.000000e+00
## Rgn.Dvn.fctrMW#WestNorthCentral#MidAtlantic 0.000000e+00
## Rgn.Dvn.fctrMW#WestNorthCentral#NewEngland 0.000000e+00
## Rgn.Dvn.fctrOT#Other 0.000000e+00
## Rgn.Dvn.fctrSH#WestSouthCentral 0.000000e+00
## Sent.Recd.Dys.nexp 0.000000e+00
## gCConsent.fctrConsentnotprovided 0.000000e+00
## gCConsent.fctrConsentprovided 0.000000e+00
## gCompany.fctrComerica 0.000000e+00
## gCompany.fctrOther 0.000000e+00
## gCompany.fctrU.S.Bancorp 0.000000e+00
## gSPrd.fctrVehicleloan 0.000000e+00
## gSPrd.fctrCheckingaccount 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.) 0.000000e+00
## gSPrd.fctrIdonotknow 0.000000e+00
## gSPrd.fctr 0.000000e+00
## gSPrd.fctrOthermortgage 0.000000e+00
## gSPrd.fctrCreditcard 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM) 0.000000e+00
## gSPrd.fctrOtherbankproduct/service 0.000000e+00
## gSPrd.fctrNon-federalstudentloan 0.000000e+00
## gSPrd.fctrFHAmortgage 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit 0.000000e+00
## gSPrd.fctrVehiclelease 0.000000e+00
## gSPrd.fctrSavingsaccount 0.000000e+00
## gSPrd.fctrFederalstudentloan 0.000000e+00
## gSPrd.fctrMortgage 0.000000e+00
## gSPrd.fctrReversemortgage 0.000000e+00
## gSPrd.fctrAuto 0.000000e+00
## gSPrd.fctrInstallmentloan 0.000000e+00
## gSPrd.fctrMedical 0.000000e+00
## gSPrd.fctrVAmortgage 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer 0.000000e+00
## gSPrd.fctrPersonallineofcredit 0.000000e+00
## gSPrd.fctrSecondmortgage 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount 0.000000e+00
## gSPrd.fctrPaydayloan 0.000000e+00
## gSPrd.fctrCheckcashing 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard 0.000000e+00
## gSPrd.fctrGeneralpurposecard 0.000000e+00
## gSPrd.fctrTitleloan 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange 0.000000e+00
## gSPrd.fctrRefundanticipationcheck 0.000000e+00
## gSPrd.fctrDebtsettlement 0.000000e+00
## gSPrd.fctrIDprepaidcard 0.000000e+00
## gSPrd.fctrCreditrepair 0.000000e+00
## gSPrd.fctrPawnloan 0.000000e+00
## gSPrd.fctrMoneyorder 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr2 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr2 0.000000e+00
## gSPrd.fctr:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMobilewallet:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOther:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr3 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr3 0.000000e+00
## gSPrd.fctr:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrIDprepaidcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOther:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr4 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr4 0.000000e+00
## gSPrd.fctr:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMobilewallet:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrIDprepaidcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr4 0.000000e+00
## imp
## Channel.fctrFax 1.000000e+02
## Response.fctrClosedwithnon-monetaryrelief 8.329310e+01
## Response.fctrClosedwithmonetaryrelief 7.885785e+01
## gSPrd.fctrMobilewallet:.clusterid.fctr3 6.997018e+01
## Datesenttocompany.month.fctr06 5.495954e+01
## gCompany.fctrAmex 5.284365e+01
## Channel.fctrPhone 4.598173e+01
## gCompany.fctrNetSpendCorporation,aTSYSCompany 4.563519e+01
## gTags.fctrServicemember 4.196423e+01
## gSPrd.fctrOther:.clusterid.fctr2 3.722203e+01
## Channel.fctrReferral 3.674772e+01
## gCompany.fctrBlackhawkNetworkHoldingsInc. 2.816743e+01
## Datesenttocompany.wkend 2.720754e+01
## gCConsent.fctrOther 2.442674e+01
## gTags.fctrOlderAmerican 2.315353e+01
## gSPrd.fctrMobilewallet 2.006931e+01
## Datesenttocompany.month.fctr05 1.985987e+01
## Datesenttocompany.month.fctr11 1.943992e+01
## Rgn.Dvn.fctrMW#EastNorthCentral 1.855754e+01
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3 1.813850e+01
## Datesenttocompany.month.fctr03 1.724040e+01
## Datesenttocompany.month.fctr12 1.706922e+01
## Datesenttocompany.month.fctr07 1.674624e+01
## Response.fctrClosed 1.642855e+01
## Rgn.Dvn.fctrMW#WestNorthCentral 1.537673e+01
## Rgn.Dvn.fctrSH#EastSouthCentral 1.503102e+01
## Rgn.Dvn.fctrWT#Mountain 1.255275e+01
## gSPrd.fctrPayrollcard 1.148085e+01
## Datesenttocompany.month.fctr09 9.426734e+00
## gCompany.fctrPayPalHoldings,Inc. 8.435248e+00
## Rgn.Dvn.fctrWT#Pacific 6.979498e+00
## gTags.fctrOlderAmericanServicemember 6.186618e+00
## Datesenttocompany.date.fctr(7,13] 5.803873e+00
## Sent.Recd.Dys.root2 4.348095e+00
## Datesenttocompany.last2.log1p 4.301632e+00
## Channel.fctrPostalmail 3.425176e+00
## gCompany.fctrCitibank 3.243715e+00
## gSPrd.fctrGiftormerchantcard 2.699154e+00
## Datesenttocompany.date.fctr(25,31] 2.697157e+00
## Datereceived.wkday.fctr2 2.453702e+00
## Datesenttocompany.last8.log1p 2.371454e+00
## Datesenttocompany.last4.log1p 1.345608e+00
## Datereceived.wkday.fctr4 5.206375e-01
## Datesenttocompany.last16.log1p 4.760548e-01
## Datesenttocompany.wkday.fctr2 3.873043e-01
## gSPrd.fctrIDprepaidcard:.clusterid.fctr2 7.205122e-02
## Sent.Recd.Dys 6.766756e-03
## Datesenttocompany.wkday.fctr4 3.993891e-04
## .pos 1.118247e-04
## ComplaintID 2.394310e-06
## .rnorm 0.000000e+00
## Datereceived.wkday.fctr1 0.000000e+00
## Datereceived.wkday.fctr3 0.000000e+00
## Datereceived.wkday.fctr5 0.000000e+00
## Datereceived.wkday.fctr6 0.000000e+00
## Datesenttocompany.date.fctr(13,19] 0.000000e+00
## Datesenttocompany.date.fctr(19,25] 0.000000e+00
## Datesenttocompany.juliandate 0.000000e+00
## Datesenttocompany.last32.log1p 0.000000e+00
## Datesenttocompany.month.fctr02 0.000000e+00
## Datesenttocompany.month.fctr04 0.000000e+00
## Datesenttocompany.month.fctr08 0.000000e+00
## Datesenttocompany.month.fctr10 0.000000e+00
## Datesenttocompany.wkday.fctr1 0.000000e+00
## Datesenttocompany.wkday.fctr3 0.000000e+00
## Datesenttocompany.wkday.fctr5 0.000000e+00
## Datesenttocompany.wkday.fctr6 0.000000e+00
## Datesenttocompany.year.fctr2015 0.000000e+00
## Datesenttocompany.year.fctr2016 0.000000e+00
## Response.fctrInprogress 0.000000e+00
## Response.fctrUntimelyresponse 0.000000e+00
## Rgn.Dvn.fctrMW#WestNorthCentral#MidAtlantic 0.000000e+00
## Rgn.Dvn.fctrMW#WestNorthCentral#NewEngland 0.000000e+00
## Rgn.Dvn.fctrOT#Other 0.000000e+00
## Rgn.Dvn.fctrSH#WestSouthCentral 0.000000e+00
## Sent.Recd.Dys.nexp 0.000000e+00
## gCConsent.fctrConsentnotprovided 0.000000e+00
## gCConsent.fctrConsentprovided 0.000000e+00
## gCompany.fctrComerica 0.000000e+00
## gCompany.fctrOther 0.000000e+00
## gCompany.fctrU.S.Bancorp 0.000000e+00
## gSPrd.fctrVehicleloan 0.000000e+00
## gSPrd.fctrCheckingaccount 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.) 0.000000e+00
## gSPrd.fctrIdonotknow 0.000000e+00
## gSPrd.fctr 0.000000e+00
## gSPrd.fctrOthermortgage 0.000000e+00
## gSPrd.fctrCreditcard 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM) 0.000000e+00
## gSPrd.fctrOtherbankproduct/service 0.000000e+00
## gSPrd.fctrNon-federalstudentloan 0.000000e+00
## gSPrd.fctrFHAmortgage 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit 0.000000e+00
## gSPrd.fctrVehiclelease 0.000000e+00
## gSPrd.fctrSavingsaccount 0.000000e+00
## gSPrd.fctrFederalstudentloan 0.000000e+00
## gSPrd.fctrMortgage 0.000000e+00
## gSPrd.fctrReversemortgage 0.000000e+00
## gSPrd.fctrAuto 0.000000e+00
## gSPrd.fctrInstallmentloan 0.000000e+00
## gSPrd.fctrMedical 0.000000e+00
## gSPrd.fctrVAmortgage 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer 0.000000e+00
## gSPrd.fctrPersonallineofcredit 0.000000e+00
## gSPrd.fctrSecondmortgage 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount 0.000000e+00
## gSPrd.fctrPaydayloan 0.000000e+00
## gSPrd.fctrCheckcashing 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard 0.000000e+00
## gSPrd.fctrGeneralpurposecard 0.000000e+00
## gSPrd.fctrTitleloan 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange 0.000000e+00
## gSPrd.fctrRefundanticipationcheck 0.000000e+00
## gSPrd.fctrDebtsettlement 0.000000e+00
## gSPrd.fctrIDprepaidcard 0.000000e+00
## gSPrd.fctrCreditrepair 0.000000e+00
## gSPrd.fctrPawnloan 0.000000e+00
## gSPrd.fctrMoneyorder 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr2 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr2 0.000000e+00
## gSPrd.fctr:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMobilewallet:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOther:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr3 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr3 0.000000e+00
## gSPrd.fctr:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrIDprepaidcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOther:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr4 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr4 0.000000e+00
## gSPrd.fctr:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMobilewallet:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrIDprepaidcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr4 0.000000e+00## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSltId, : Limiting important feature scatter plots to 5 out of 26
## Loading required package: lazyeval## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## ComplaintID CDisputed.fctr CDisputed.fctr.Low.cor.X..rcv.glmnet.prob
## 1 1623265 Y 0.02058124
## 2 1622364 Y 0.02671534
## 3 1630068 Y 0.02717783
## 4 1245156 Y 0.04802992
## 5 1136031 Y 0.05408014
## 6 1636977 Y 0.05460852
## CDisputed.fctr.Low.cor.X..rcv.glmnet
## 1 N
## 2 N
## 3 N
## 4 N
## 5 N
## 6 N
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err.abs
## 1 0.9794188
## 2 0.9732847
## 3 0.9728222
## 4 0.9519701
## 5 0.9459199
## 6 0.9453915
## CDisputed.fctr.Low.cor.X..rcv.glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.error
## 1 -0.4794188
## 2 -0.4732847
## 3 -0.4728222
## 4 -0.4519701
## 5 -0.4459199
## 6 -0.4453915
## ComplaintID CDisputed.fctr CDisputed.fctr.Low.cor.X..rcv.glmnet.prob
## 52 2057714 Y 0.2585564
## 59 1711162 Y 0.2907839
## 61 1449967 Y 0.2989173
## 64 1555274 Y 0.3097251
## 65 2008379 Y 0.3110218
## 81 1501714 Y 0.4257045
## CDisputed.fctr.Low.cor.X..rcv.glmnet
## 52 N
## 59 N
## 61 N
## 64 N
## 65 N
## 81 N
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err
## 52 TRUE
## 59 TRUE
## 61 TRUE
## 64 TRUE
## 65 TRUE
## 81 TRUE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err.abs
## 52 0.7414436
## 59 0.7092161
## 61 0.7010827
## 64 0.6902749
## 65 0.6889782
## 81 0.5742955
## CDisputed.fctr.Low.cor.X..rcv.glmnet.is.acc
## 52 FALSE
## 59 FALSE
## 61 FALSE
## 64 FALSE
## 65 FALSE
## 81 FALSE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.accurate
## 52 FALSE
## 59 FALSE
## 61 FALSE
## 64 FALSE
## 65 FALSE
## 81 FALSE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.error
## 52 -0.24144362
## 59 -0.20921609
## 61 -0.20108275
## 64 -0.19027493
## 65 -0.18897816
## 81 -0.07429545
## ComplaintID CDisputed.fctr CDisputed.fctr.Low.cor.X..rcv.glmnet.prob
## 79 2075942 Y 0.4106862
## 80 951011 Y 0.4226405
## 81 1501714 Y 0.4257045
## 82 1705947 Y 0.4574256
## 83 1247838 N 0.5221573
## 84 1275358 N 0.5673760
## CDisputed.fctr.Low.cor.X..rcv.glmnet
## 79 N
## 80 N
## 81 N
## 82 N
## 83 Y
## 84 Y
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err
## 79 TRUE
## 80 TRUE
## 81 TRUE
## 82 TRUE
## 83 TRUE
## 84 TRUE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err.abs
## 79 0.5893138
## 80 0.5773595
## 81 0.5742955
## 82 0.5425744
## 83 0.5221573
## 84 0.5673760
## CDisputed.fctr.Low.cor.X..rcv.glmnet.is.acc
## 79 FALSE
## 80 FALSE
## 81 FALSE
## 82 FALSE
## 83 FALSE
## 84 FALSE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.accurate
## 79 FALSE
## 80 FALSE
## 81 FALSE
## 82 FALSE
## 83 FALSE
## 84 FALSE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.error
## 79 -0.08931384
## 80 -0.07735947
## 81 -0.07429545
## 82 -0.04257445
## 83 0.02215734
## 84 0.06737600## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## ComplaintID CDisputed.fctr CDisputed.fctr.Low.cor.X..rcv.glmnet.prob
## 1 1623265 Y 0.02058124
## 2 1622364 Y 0.02671534
## 3 1630068 Y 0.02717783
## 4 1245156 Y 0.04802992
## 5 1136031 Y 0.05408014
## 6 1636977 Y 0.05460852
## CDisputed.fctr.Low.cor.X..rcv.glmnet
## 1 N
## 2 N
## 3 N
## 4 N
## 5 N
## 6 N
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err.abs
## 1 0.9794188
## 2 0.9732847
## 3 0.9728222
## 4 0.9519701
## 5 0.9459199
## 6 0.9453915
## CDisputed.fctr.Low.cor.X..rcv.glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.error
## 1 -0.4794188
## 2 -0.4732847
## 3 -0.4728222
## 4 -0.4519701
## 5 -0.4459199
## 6 -0.4453915
## ComplaintID CDisputed.fctr CDisputed.fctr.Low.cor.X..rcv.glmnet.prob
## 23 1157233 Y 0.1518147
## 31 1774058 Y 0.1882378
## 34 1406750 Y 0.1981061
## 58 1807001 Y 0.2888150
## 61 1449967 Y 0.2989173
## 80 951011 Y 0.4226405
## CDisputed.fctr.Low.cor.X..rcv.glmnet
## 23 N
## 31 N
## 34 N
## 58 N
## 61 N
## 80 N
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err
## 23 TRUE
## 31 TRUE
## 34 TRUE
## 58 TRUE
## 61 TRUE
## 80 TRUE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err.abs
## 23 0.8481853
## 31 0.8117622
## 34 0.8018939
## 58 0.7111850
## 61 0.7010827
## 80 0.5773595
## CDisputed.fctr.Low.cor.X..rcv.glmnet.is.acc
## 23 FALSE
## 31 FALSE
## 34 FALSE
## 58 FALSE
## 61 FALSE
## 80 FALSE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.accurate
## 23 FALSE
## 31 FALSE
## 34 FALSE
## 58 FALSE
## 61 FALSE
## 80 FALSE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.error
## 23 -0.34818532
## 31 -0.31176217
## 34 -0.30189387
## 58 -0.21118498
## 61 -0.20108275
## 80 -0.07735947
## ComplaintID CDisputed.fctr CDisputed.fctr.Low.cor.X..rcv.glmnet.prob
## 79 2075942 Y 0.4106862
## 80 951011 Y 0.4226405
## 81 1501714 Y 0.4257045
## 82 1705947 Y 0.4574256
## 83 1247838 N 0.5221573
## 84 1275358 N 0.5673760
## CDisputed.fctr.Low.cor.X..rcv.glmnet
## 79 N
## 80 N
## 81 N
## 82 N
## 83 Y
## 84 Y
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err
## 79 TRUE
## 80 TRUE
## 81 TRUE
## 82 TRUE
## 83 TRUE
## 84 TRUE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err.abs
## 79 0.5893138
## 80 0.5773595
## 81 0.5742955
## 82 0.5425744
## 83 0.5221573
## 84 0.5673760
## CDisputed.fctr.Low.cor.X..rcv.glmnet.is.acc
## 79 FALSE
## 80 FALSE
## 81 FALSE
## 82 FALSE
## 83 FALSE
## 84 FALSE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.accurate
## 79 FALSE
## 80 FALSE
## 81 FALSE
## 82 FALSE
## 83 FALSE
## 84 FALSE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.error
## 79 -0.08931384
## 80 -0.07735947
## 81 -0.07429545
## 82 -0.04257445
## 83 0.02215734
## 84 0.06737600## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.
## gSPrd.fctr .n.OOB .n.Fit
## Giftormerchantcard Giftormerchantcard 74 200
## Mobilewallet Mobilewallet 64 242
## Other Other 32 149
## IDprepaidcard IDprepaidcard 29 137
## Governmentbenefitpaymentcard Governmentbenefitpaymentcard 76 185
## Generalpurposecard Generalpurposecard 241 1100
## Payrollcard Payrollcard 59 281
## .n.Tst .freqRatio.Fit .freqRatio.OOB
## Giftormerchantcard 21 0.08718396 0.12869565
## Mobilewallet 18 0.10549259 0.11130435
## Other 9 0.06495205 0.05565217
## IDprepaidcard 8 0.05972101 0.05043478
## Governmentbenefitpaymentcard 22 0.08064516 0.13217391
## Generalpurposecard 69 0.47951177 0.41913043
## Payrollcard 18 0.12249346 0.10260870
## .freqRatio.Tst err.abs.fit.sum
## Giftormerchantcard 0.12727273 58.96237
## Mobilewallet 0.10909091 72.91780
## Other 0.05454545 38.57401
## IDprepaidcard 0.04848485 29.97656
## Governmentbenefitpaymentcard 0.13333333 40.79012
## Generalpurposecard 0.41818182 246.61968
## Payrollcard 0.10909091 34.73402
## err.abs.fit.mean .n.fit err.abs.OOB.sum
## Giftormerchantcard 0.2948118 200 21.714241
## Mobilewallet 0.3013132 242 17.462991
## Other 0.2588859 149 7.921361
## IDprepaidcard 0.2188070 137 6.896103
## Governmentbenefitpaymentcard 0.2204871 185 17.945749
## Generalpurposecard 0.2241997 1100 56.641561
## Payrollcard 0.1236086 281 8.086594
## err.abs.OOB.mean
## Giftormerchantcard 0.2934357
## Mobilewallet 0.2728592
## Other 0.2475425
## IDprepaidcard 0.2377967
## Governmentbenefitpaymentcard 0.2361283
## Generalpurposecard 0.2350272
## Payrollcard 0.1370609
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 575.000000 2294.000000 165.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 522.574552 1.642113
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 2294.000000 136.668600 1.659851## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 156.021 NA NA## label step_major step_minor label_minor bgn end elapsed
## 4 fit.models 2 2 2 149.418 156.028 6.61
## 5 fit.models 2 3 3 156.029 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 5 fit.models 2 3 3 156.029 157.948
## 6 fit.data.training 3 0 0 157.949 NA
## elapsed
## 5 1.919
## 6 NA#myloadChunk(glbChunks$inpFilePathName, keepSpec = c(NULL), dropSpec = c(NULL)); glb2Sav()Step 3.0: fit data training
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Trn.Low.cor.X###glmnet"
## [1] " indepVar: Datesenttocompany.last8.log1p,Datesenttocompany.last4.log1p,Datesenttocompany.last2.log1p,Datesenttocompany.last16.log1p,Datesenttocompany.last32.log1p,.pos,gCompany.fctr,gTags.fctr,Sent.Recd.Dys.nexp,Rgn.Dvn.fctr,Datesenttocompany.year.fctr,ComplaintID,Datereceived.wkday.fctr,Datesenttocompany.wkday.fctr,gCConsent.fctr,.rnorm,Datesenttocompany.wkend,Channel.fctr,gSPrd.fctr,Datesenttocompany.month.fctr,Datesenttocompany.juliandate,Datesenttocompany.date.fctr,Sent.Recd.Dys,Sent.Recd.Dys.root2,Response.fctr,gSPrd.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.650000 secs"
## Fitting alpha = 0.775, lambda = 0.00573 on full training set
## [1] "myfit_mdl: train complete: 1.591000 secs"
## alpha lambda
## 1 0.775 0.005728268
## Length Class Mode
## a0 71 -none- numeric
## beta 18247 dgCMatrix S4
## df 71 -none- numeric
## dim 2 -none- numeric
## lambda 71 -none- numeric
## dev.ratio 71 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 257 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -2.363814e+00
## .pos
## 1.117139e-06
## Channel.fctrFax
## -1.077880e+00
## Channel.fctrPhone
## -5.448066e-01
## Channel.fctrPostalmail
## -2.590532e-01
## Channel.fctrReferral
## -2.314804e-01
## Datereceived.wkday.fctr4
## 5.481299e-02
## Datesenttocompany.date.fctr(13,19]
## -3.814596e-02
## Datesenttocompany.last16.log1p
## 1.488344e-02
## Datesenttocompany.last2.log1p
## 4.699619e-02
## Datesenttocompany.last4.log1p
## 1.172091e-02
## Datesenttocompany.last8.log1p
## 1.214202e-02
## Datesenttocompany.month.fctr03
## -1.008800e-01
## Datesenttocompany.month.fctr05
## 2.882872e-01
## Datesenttocompany.month.fctr06
## -3.729237e-01
## Datesenttocompany.month.fctr07
## 2.810651e-01
## Datesenttocompany.month.fctr08
## 2.538584e-01
## Datesenttocompany.month.fctr12
## 2.218416e-01
## Datesenttocompany.wkday.fctr4
## 2.145076e-02
## Datesenttocompany.wkend
## -2.966505e-01
## Datesenttocompany.year.fctr2015
## -1.543883e-02
## Response.fctrClosed
## -2.571291e-01
## Response.fctrClosedwithmonetaryrelief
## -8.258984e-01
## Response.fctrClosedwithnon-monetaryrelief
## -1.042770e+00
## Rgn.Dvn.fctrMW#WestNorthCentral
## -7.236254e-02
## Rgn.Dvn.fctrMW#WestNorthCentral#NewEngland
## -8.491669e-03
## Rgn.Dvn.fctrSH#EastSouthCentral
## -3.521139e-01
## Rgn.Dvn.fctrWT#Mountain
## 5.714978e-02
## Rgn.Dvn.fctrWT#Pacific
## 1.087005e-01
## Sent.Recd.Dys
## -6.570704e-04
## Sent.Recd.Dys.root2
## -4.085045e-02
## gCConsent.fctrOther
## 4.291348e-01
## gCompany.fctrAmex
## 5.098867e-01
## gCompany.fctrBlackhawkNetworkHoldingsInc.
## 2.998486e-01
## gCompany.fctrNetSpendCorporation,aTSYSCompany
## -6.263926e-01
## gCompany.fctrOther
## 1.766983e-02
## gCompany.fctrPayPalHoldings,Inc.
## 5.661215e-02
## gSPrd.fctrMobilewallet
## 1.548139e-01
## gSPrd.fctrPayrollcard
## -6.447514e-02
## gTags.fctrOlderAmerican
## 2.713868e-01
## gTags.fctrServicemember
## 3.955066e-01
## gSPrd.fctrOther:.clusterid.fctr2
## 3.398852e-01
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr2
## -1.950539e-01
## gSPrd.fctrMobilewallet:.clusterid.fctr3
## 7.078459e-01
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3
## -3.024532e-02
## gSPrd.fctrOther:.clusterid.fctr4
## -3.003160e-02
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -2.381387e+00
## .pos
## 1.145067e-06
## Channel.fctrFax
## -1.196003e+00
## Channel.fctrPhone
## -5.663735e-01
## Channel.fctrPostalmail
## -2.957399e-01
## Channel.fctrReferral
## -2.566862e-01
## Datereceived.wkday.fctr4
## 5.658750e-02
## Datesenttocompany.date.fctr(13,19]
## -4.689874e-02
## Datesenttocompany.last16.log1p
## 1.569183e-02
## Datesenttocompany.last2.log1p
## 4.795155e-02
## Datesenttocompany.last4.log1p
## 1.164507e-02
## Datesenttocompany.last8.log1p
## 1.124085e-02
## Datesenttocompany.month.fctr02
## 4.017405e-04
## Datesenttocompany.month.fctr03
## -1.131642e-01
## Datesenttocompany.month.fctr05
## 2.946263e-01
## Datesenttocompany.month.fctr06
## -3.901378e-01
## Datesenttocompany.month.fctr07
## 2.851608e-01
## Datesenttocompany.month.fctr08
## 2.590569e-01
## Datesenttocompany.month.fctr09
## 8.308771e-03
## Datesenttocompany.month.fctr12
## 2.314669e-01
## Datesenttocompany.wkday.fctr4
## 2.507203e-02
## Datesenttocompany.wkend
## -3.144910e-01
## Datesenttocompany.year.fctr2015
## -1.736749e-02
## Response.fctrClosed
## -2.868298e-01
## Response.fctrClosedwithmonetaryrelief
## -8.374756e-01
## Response.fctrClosedwithnon-monetaryrelief
## -1.066734e+00
## Rgn.Dvn.fctrMW#WestNorthCentral
## -8.932852e-02
## Rgn.Dvn.fctrMW#WestNorthCentral#MidAtlantic
## 8.681076e-03
## Rgn.Dvn.fctrMW#WestNorthCentral#NewEngland
## -2.496821e-02
## Rgn.Dvn.fctrSH#EastSouthCentral
## -3.768317e-01
## Rgn.Dvn.fctrWT#Mountain
## 6.737365e-02
## Rgn.Dvn.fctrWT#Pacific
## 1.132416e-01
## Sent.Recd.Dys
## -1.347848e-03
## Sent.Recd.Dys.root2
## -3.576567e-02
## gCConsent.fctrOther
## 4.423849e-01
## gCompany.fctrAmex
## 5.172696e-01
## gCompany.fctrBlackhawkNetworkHoldingsInc.
## 3.116585e-01
## gCompany.fctrNetSpendCorporation,aTSYSCompany
## -6.392033e-01
## gCompany.fctrOther
## 2.956902e-02
## gCompany.fctrPayPalHoldings,Inc.
## 7.921474e-02
## gSPrd.fctrMobilewallet
## 1.534095e-01
## gSPrd.fctrPayrollcard
## -7.399609e-02
## gTags.fctrOlderAmerican
## 2.845433e-01
## gTags.fctrOlderAmericanServicemember
## 1.771368e-02
## gTags.fctrServicemember
## 4.108728e-01
## gSPrd.fctrOther:.clusterid.fctr2
## 3.675596e-01
## gSPrd.fctrMobilewallet:.clusterid.fctr2
## 1.214857e-02
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr2
## -2.218590e-01
## gSPrd.fctrMobilewallet:.clusterid.fctr3
## 7.339463e-01
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3
## -3.666558e-02
## gSPrd.fctrOther:.clusterid.fctr4
## -1.189054e-01
## [1] "myfit_mdl: train diagnostics complete: 1.623000 secs"
## Prediction
## Reference N Y
## N 2401 31
## Y 388 49
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.539561e-01 1.494616e-01 8.404989e-01 8.666868e-01 8.476821e-01
## AccuracyPValue McnemarPValue
## 1.818781e-01 9.529990e-68
## [1] "myfit_mdl: predict complete: 5.553000 secs"
## id
## 1 Trn.Low.cor.X###glmnet
## feats
## 1 Datesenttocompany.last8.log1p,Datesenttocompany.last4.log1p,Datesenttocompany.last2.log1p,Datesenttocompany.last16.log1p,Datesenttocompany.last32.log1p,.pos,gCompany.fctr,gTags.fctr,Sent.Recd.Dys.nexp,Rgn.Dvn.fctr,Datesenttocompany.year.fctr,ComplaintID,Datereceived.wkday.fctr,Datesenttocompany.wkday.fctr,gCConsent.fctr,.rnorm,Datesenttocompany.wkend,Channel.fctr,gSPrd.fctr,Datesenttocompany.month.fctr,Datesenttocompany.juliandate,Datesenttocompany.date.fctr,Sent.Recd.Dys,Sent.Recd.Dys.root2,Response.fctr,gSPrd.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.902 0.35
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5166619 0.9967105 0.03661327 0.7692937
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.1895551 0.8539561
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8404989 0.8666868 0.1494616
## min.log.loss.mean.fit
## 1 0.3654545
## [1] "myfit_mdl: exit: 5.568000 secs"
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Trn.Low.cor.X##rcv#glmnet"
## [1] " indepVar: Datesenttocompany.last8.log1p,Datesenttocompany.last4.log1p,Datesenttocompany.last2.log1p,Datesenttocompany.last16.log1p,Datesenttocompany.last32.log1p,.pos,gCompany.fctr,gTags.fctr,Sent.Recd.Dys.nexp,Rgn.Dvn.fctr,Datesenttocompany.year.fctr,ComplaintID,Datereceived.wkday.fctr,Datesenttocompany.wkday.fctr,gCConsent.fctr,.rnorm,Datesenttocompany.wkend,Channel.fctr,gSPrd.fctr,Datesenttocompany.month.fctr,Datesenttocompany.juliandate,Datesenttocompany.date.fctr,Sent.Recd.Dys,Sent.Recd.Dys.root2,Response.fctr,gSPrd.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.641000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00548 on full training set
## [1] "myfit_mdl: train complete: 15.121000 secs"## Warning in myfit_mdl(mdl_specs_lst = thsRslSpc, indepVar = mdlIndepVar, :
## model's bestTune found at an extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 70 -none- numeric
## beta 17990 dgCMatrix S4
## df 70 -none- numeric
## dim 2 -none- numeric
## lambda 70 -none- numeric
## dev.ratio 70 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 257 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -2.333167e+00
## .pos
## 1.075346e-06
## Channel.fctrFax
## -8.603393e-01
## Channel.fctrPhone
## -5.081678e-01
## Channel.fctrPostalmail
## -1.892753e-01
## Channel.fctrReferral
## -1.835587e-01
## Datereceived.wkday.fctr4
## 6.141197e-02
## Datesenttocompany.date.fctr(13,19]
## -1.810639e-02
## Datesenttocompany.last16.log1p
## 1.310480e-02
## Datesenttocompany.last2.log1p
## 4.614203e-02
## Datesenttocompany.last4.log1p
## 1.120192e-02
## Datesenttocompany.last8.log1p
## 1.275053e-02
## Datesenttocompany.month.fctr03
## -7.340947e-02
## Datesenttocompany.month.fctr05
## 2.774988e-01
## Datesenttocompany.month.fctr06
## -3.391763e-01
## Datesenttocompany.month.fctr07
## 2.759391e-01
## Datesenttocompany.month.fctr08
## 2.449098e-01
## Datesenttocompany.month.fctr12
## 2.038299e-01
## Datesenttocompany.wkday.fctr4
## 7.444111e-04
## Datesenttocompany.wkend
## -2.658784e-01
## Datesenttocompany.year.fctr2015
## -6.930234e-03
## Response.fctrClosed
## -2.016446e-01
## Response.fctrClosedwithmonetaryrelief
## -8.195824e-01
## Response.fctrClosedwithnon-monetaryrelief
## -1.025821e+00
## Rgn.Dvn.fctrMW#WestNorthCentral
## -3.090724e-02
## Rgn.Dvn.fctrSH#EastSouthCentral
## -3.051227e-01
## Rgn.Dvn.fctrWT#Mountain
## 3.456012e-02
## Rgn.Dvn.fctrWT#Pacific
## 1.005103e-01
## Sent.Recd.Dys.root2
## -4.422287e-02
## gCConsent.fctrOther
## 4.009436e-01
## gCompany.fctrAmex
## 4.977848e-01
## gCompany.fctrBlackhawkNetworkHoldingsInc.
## 2.743479e-01
## gCompany.fctrNetSpendCorporation,aTSYSCompany
## -6.083685e-01
## gCompany.fctrPayPalHoldings,Inc.
## 1.535411e-02
## gSPrd.fctrMobilewallet
## 1.501709e-01
## gSPrd.fctrPayrollcard
## -4.116059e-02
## gTags.fctrOlderAmerican
## 2.422716e-01
## gTags.fctrServicemember
## 3.697581e-01
## gSPrd.fctrOther:.clusterid.fctr2
## 2.849640e-01
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr2
## -1.349413e-01
## gSPrd.fctrMobilewallet:.clusterid.fctr3
## 6.700160e-01
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3
## -1.391773e-02
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -2.345631e+00
## .pos
## 1.110493e-06
## Channel.fctrFax
## -9.866858e-01
## Channel.fctrPhone
## -5.339030e-01
## Channel.fctrPostalmail
## -2.291609e-01
## Channel.fctrReferral
## -2.125019e-01
## Datereceived.wkday.fctr4
## 6.869761e-02
## Datesenttocompany.date.fctr(13,19]
## -2.892870e-02
## Datesenttocompany.last16.log1p
## 1.415753e-02
## Datesenttocompany.last2.log1p
## 4.712009e-02
## Datesenttocompany.last4.log1p
## 1.130661e-02
## Datesenttocompany.last8.log1p
## 1.222803e-02
## Datesenttocompany.month.fctr03
## -8.835903e-02
## Datesenttocompany.month.fctr05
## 2.830027e-01
## Datesenttocompany.month.fctr06
## -3.612767e-01
## Datesenttocompany.month.fctr07
## 2.784572e-01
## Datesenttocompany.month.fctr08
## 2.487851e-01
## Datesenttocompany.month.fctr12
## 2.136008e-01
## Datesenttocompany.wkday.fctr4
## 7.102097e-04
## Datesenttocompany.wkend
## -2.866518e-01
## Datesenttocompany.year.fctr2015
## -8.767422e-03
## Response.fctrClosed
## -2.288318e-01
## Response.fctrClosedwithmonetaryrelief
## -8.333483e-01
## Response.fctrClosedwithnon-monetaryrelief
## -1.052720e+00
## Rgn.Dvn.fctrMW#WestNorthCentral
## -5.203702e-02
## Rgn.Dvn.fctrSH#EastSouthCentral
## -3.323242e-01
## Rgn.Dvn.fctrWT#Mountain
## 4.622561e-02
## Rgn.Dvn.fctrWT#Pacific
## 1.050098e-01
## Sent.Recd.Dys
## -1.451120e-05
## Sent.Recd.Dys.root2
## -4.416892e-02
## gCConsent.fctrOther
## 4.167729e-01
## gCompany.fctrAmex
## 5.025891e-01
## gCompany.fctrBlackhawkNetworkHoldingsInc.
## 2.846731e-01
## gCompany.fctrNetSpendCorporation,aTSYSCompany
## -6.278474e-01
## gCompany.fctrPayPalHoldings,Inc.
## 3.696520e-02
## gSPrd.fctrMobilewallet
## 1.528836e-01
## gSPrd.fctrPayrollcard
## -5.114277e-02
## gTags.fctrOlderAmerican
## 2.589077e-01
## gTags.fctrServicemember
## 3.858623e-01
## gSPrd.fctrOther:.clusterid.fctr2
## 3.190944e-01
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr2
## -1.678997e-01
## gSPrd.fctrMobilewallet:.clusterid.fctr3
## 6.946010e-01
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3
## -2.195342e-02
## [1] "myfit_mdl: train diagnostics complete: 15.557000 secs"
## Prediction
## Reference N Y
## N 2405 27
## Y 394 43
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.532590e-01 1.331661e-01 8.397770e-01 8.660159e-01 8.476821e-01
## AccuracyPValue McnemarPValue
## 2.109228e-01 3.599080e-71
## [1] "myfit_mdl: predict complete: 19.657000 secs"
## id
## 1 Trn.Low.cor.X##rcv#glmnet
## feats
## 1 Datesenttocompany.last8.log1p,Datesenttocompany.last4.log1p,Datesenttocompany.last2.log1p,Datesenttocompany.last16.log1p,Datesenttocompany.last32.log1p,.pos,gCompany.fctr,gTags.fctr,Sent.Recd.Dys.nexp,Rgn.Dvn.fctr,Datesenttocompany.year.fctr,ComplaintID,Datereceived.wkday.fctr,Datesenttocompany.wkday.fctr,gCConsent.fctr,.rnorm,Datesenttocompany.wkend,Channel.fctr,gSPrd.fctr,Datesenttocompany.month.fctr,Datesenttocompany.juliandate,Datesenttocompany.date.fctr,Sent.Recd.Dys,Sent.Recd.Dys.root2,Response.fctr,gSPrd.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 14.451 0.344
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5140518 0.9983553 0.02974828 0.7670543
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.1696252 0.8488446
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.839777 0.8660159 0.0368382
## min.log.loss.mean.fit max.AccuracySD.fit max.KappaSD.fit
## 1 0.3669598 0.004696781 0.0371336
## [1] "myfit_mdl: exit: 19.674000 secs"## label step_major step_minor label_minor bgn end
## 6 fit.data.training 3 0 0 157.949 183.425
## 7 fit.data.training 3 1 1 183.426 NA
## elapsed
## 6 25.476
## 7 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSltId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFnlNslId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFnlNslId)$feats, ","))
if (glb_is_classification)
# mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
# mdlEnsembleComps <- gsub(paste0("^",
# gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
# "", mdlEnsembleComps)
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$prob %in% mdlEnsembleComps)] else
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlEnsembleComps)]
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
# glbMdlFnlNsl uses the same coefficients as glbMdlSlt,
# so copy the "Final" columns into "non-Final" columns
glbObsTrn[, gsub("Trn.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsTrn[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
glbObsNew[, gsub("Trn.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsNew[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFnlNslId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFnlNslId, :
## Using default probability threshold: 0.5glb_featsimp_df <- myget_feats_importance(mdl=glbMdlFnlNsl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFnlNslId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## Low.cor.X..rcv.glmnet.imp
## Channel.fctrFax 1.000000e+02
## Response.fctrClosedwithnon-monetaryrelief 8.329310e+01
## Response.fctrClosedwithmonetaryrelief 7.885785e+01
## gSPrd.fctrMobilewallet:.clusterid.fctr3 6.997018e+01
## gCompany.fctrNetSpendCorporation,aTSYSCompany 4.563519e+01
## Channel.fctrPhone 4.598173e+01
## gCompany.fctrAmex 5.284365e+01
## gCConsent.fctrOther 2.442674e+01
## gTags.fctrServicemember 4.196423e+01
## Datesenttocompany.month.fctr06 5.495954e+01
## Rgn.Dvn.fctrSH#EastSouthCentral 1.503102e+01
## gSPrd.fctrOther:.clusterid.fctr2 3.722203e+01
## Datesenttocompany.wkend 2.720754e+01
## gCompany.fctrBlackhawkNetworkHoldingsInc. 2.816743e+01
## Datesenttocompany.month.fctr05 1.985987e+01
## Channel.fctrPostalmail 3.425176e+00
## Datesenttocompany.month.fctr07 1.674624e+01
## gTags.fctrOlderAmerican 2.315353e+01
## Response.fctrClosed 1.642855e+01
## Datesenttocompany.month.fctr08 0.000000e+00
## Channel.fctrReferral 3.674772e+01
## Datesenttocompany.month.fctr12 1.706922e+01
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMobilewallet 2.006931e+01
## Rgn.Dvn.fctrWT#Pacific 6.979498e+00
## Datesenttocompany.month.fctr03 1.724040e+01
## gSPrd.fctrOther:.clusterid.fctr4 0.000000e+00
## Rgn.Dvn.fctrMW#WestNorthCentral 1.537673e+01
## gCompany.fctrPayPalHoldings,Inc. 8.435248e+00
## gSPrd.fctrPayrollcard 1.148085e+01
## Rgn.Dvn.fctrWT#Mountain 1.255275e+01
## Datereceived.wkday.fctr4 5.206375e-01
## Datesenttocompany.last2.log1p 4.301632e+00
## Datesenttocompany.date.fctr(13,19] 0.000000e+00
## Sent.Recd.Dys.root2 4.348095e+00
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3 1.813850e+01
## gCompany.fctrOther 0.000000e+00
## Datesenttocompany.wkday.fctr4 3.993891e-04
## Rgn.Dvn.fctrMW#WestNorthCentral#NewEngland 0.000000e+00
## Datesenttocompany.year.fctr2015 0.000000e+00
## Datesenttocompany.last16.log1p 4.760548e-01
## gTags.fctrOlderAmericanServicemember 6.186618e+00
## Datesenttocompany.last4.log1p 1.345608e+00
## Datesenttocompany.last8.log1p 2.371454e+00
## gSPrd.fctrMobilewallet:.clusterid.fctr2 0.000000e+00
## Rgn.Dvn.fctrMW#WestNorthCentral#MidAtlantic 0.000000e+00
## Datesenttocompany.month.fctr09 9.426734e+00
## Sent.Recd.Dys 6.766756e-03
## Datesenttocompany.month.fctr02 0.000000e+00
## .pos 1.118247e-04
## .rnorm 0.000000e+00
## ComplaintID 2.394310e-06
## Datereceived.wkday.fctr1 0.000000e+00
## Datereceived.wkday.fctr2 2.453702e+00
## Datereceived.wkday.fctr3 0.000000e+00
## Datereceived.wkday.fctr5 0.000000e+00
## Datereceived.wkday.fctr6 0.000000e+00
## Datesenttocompany.date.fctr(19,25] 0.000000e+00
## Datesenttocompany.date.fctr(25,31] 2.697157e+00
## Datesenttocompany.date.fctr(7,13] 5.803873e+00
## Datesenttocompany.juliandate 0.000000e+00
## Datesenttocompany.last32.log1p 0.000000e+00
## Datesenttocompany.month.fctr04 0.000000e+00
## Datesenttocompany.month.fctr10 0.000000e+00
## Datesenttocompany.month.fctr11 1.943992e+01
## Datesenttocompany.wkday.fctr1 0.000000e+00
## Datesenttocompany.wkday.fctr2 3.873043e-01
## Datesenttocompany.wkday.fctr3 0.000000e+00
## Datesenttocompany.wkday.fctr5 0.000000e+00
## Datesenttocompany.wkday.fctr6 0.000000e+00
## Datesenttocompany.year.fctr2016 0.000000e+00
## Response.fctrInprogress 0.000000e+00
## Response.fctrUntimelyresponse 0.000000e+00
## Rgn.Dvn.fctrMW#EastNorthCentral 1.855754e+01
## Rgn.Dvn.fctrOT#Other 0.000000e+00
## Rgn.Dvn.fctrSH#WestSouthCentral 0.000000e+00
## Sent.Recd.Dys.nexp 0.000000e+00
## gCConsent.fctrConsentnotprovided 0.000000e+00
## gCConsent.fctrConsentprovided 0.000000e+00
## gCompany.fctrCitibank 3.243715e+00
## gCompany.fctrComerica 0.000000e+00
## gCompany.fctrU.S.Bancorp 0.000000e+00
## gSPrd.fctr 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctr:.clusterid.fctr2 0.000000e+00
## gSPrd.fctr:.clusterid.fctr3 0.000000e+00
## gSPrd.fctr:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrAuto 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCheckcashing 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCheckingaccount 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM) 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr2 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr3 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr4 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCreditcard 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCreditrepair 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrDebtsettlement 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFHAmortgage 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFederalstudentloan 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGeneralpurposecard 0.000000e+00
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGiftormerchantcard 2.699154e+00
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrIDprepaidcard 0.000000e+00
## gSPrd.fctrIDprepaidcard:.clusterid.fctr2 7.205122e-02
## gSPrd.fctrIDprepaidcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrIDprepaidcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrIdonotknow 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrInstallmentloan 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMedical 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMobilewallet:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMoneyorder 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMortgage 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrNon-federalstudentloan 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.) 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOther:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOtherbankproduct/service 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOthermortgage 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPawnloan 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPaydayloan 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPersonallineofcredit 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrRefundanticipationcheck 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrReversemortgage 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrSavingsaccount 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrSecondmortgage 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrTitleloan 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVAmortgage 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVehiclelease 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVehicleloan 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr4 0.000000e+00
## Trn.Low.cor.X...glmnet.imp
## Channel.fctrFax 1.000000e+02
## Response.fctrClosedwithnon-monetaryrelief 9.048980e+01
## Response.fctrClosedwithmonetaryrelief 7.115750e+01
## gSPrd.fctrMobilewallet:.clusterid.fctr3 6.210649e+01
## gCompany.fctrNetSpendCorporation,aTSYSCompany 5.424758e+01
## Channel.fctrPhone 4.790375e+01
## gCompany.fctrAmex 4.394697e+01
## gCConsent.fctrOther 3.747417e+01
## gTags.fctrServicemember 3.475599e+01
## Datesenttocompany.month.fctr06 3.296016e+01
## Rgn.Dvn.fctrSH#EastSouthCentral 3.170696e+01
## gSPrd.fctrOther:.clusterid.fctr2 3.086994e+01
## Datesenttocompany.wkend 2.650603e+01
## gCompany.fctrBlackhawkNetworkHoldingsInc. 2.636093e+01
## Datesenttocompany.month.fctr05 2.499726e+01
## Channel.fctrPostalmail 2.460808e+01
## Datesenttocompany.month.fctr07 2.422671e+01
## gTags.fctrOlderAmerican 2.402958e+01
## Response.fctrClosed 2.396048e+01
## Datesenttocompany.month.fctr08 2.198540e+01
## Channel.fctrReferral 2.146433e+01
## Datesenttocompany.month.fctr12 1.956448e+01
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr2 1.847199e+01
## gSPrd.fctrMobilewallet 1.309092e+01
## Rgn.Dvn.fctrWT#Pacific 9.574300e+00
## Datesenttocompany.month.fctr03 9.444201e+00
## gSPrd.fctrOther:.clusterid.fctr4 8.711653e+00
## Rgn.Dvn.fctrMW#WestNorthCentral 7.339031e+00
## gCompany.fctrPayPalHoldings,Inc. 6.387562e+00
## gSPrd.fctrPayrollcard 6.151655e+00
## Rgn.Dvn.fctrWT#Mountain 5.576296e+00
## Datereceived.wkday.fctr4 4.792224e+00
## Datesenttocompany.last2.log1p 4.069618e+00
## Datesenttocompany.date.fctr(13,19] 3.855561e+00
## Sent.Recd.Dys.root2 3.127879e+00
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3 3.021032e+00
## gCompany.fctrOther 2.329106e+00
## Datesenttocompany.wkday.fctr4 2.078054e+00
## Rgn.Dvn.fctrMW#WestNorthCentral#NewEngland 1.864166e+00
## Datesenttocompany.year.fctr2015 1.448724e+00
## Datesenttocompany.last16.log1p 1.323848e+00
## gTags.fctrOlderAmericanServicemember 1.226441e+00
## Datesenttocompany.last4.log1p 9.932198e-01
## Datesenttocompany.last8.log1p 9.719498e-01
## gSPrd.fctrMobilewallet:.clusterid.fctr2 8.411298e-01
## Rgn.Dvn.fctrMW#WestNorthCentral#MidAtlantic 6.010509e-01
## Datesenttocompany.month.fctr09 5.752737e-01
## Sent.Recd.Dys 1.038013e-01
## Datesenttocompany.month.fctr02 2.781527e-02
## .pos 9.709951e-05
## .rnorm 0.000000e+00
## ComplaintID 0.000000e+00
## Datereceived.wkday.fctr1 0.000000e+00
## Datereceived.wkday.fctr2 0.000000e+00
## Datereceived.wkday.fctr3 0.000000e+00
## Datereceived.wkday.fctr5 0.000000e+00
## Datereceived.wkday.fctr6 0.000000e+00
## Datesenttocompany.date.fctr(19,25] 0.000000e+00
## Datesenttocompany.date.fctr(25,31] 0.000000e+00
## Datesenttocompany.date.fctr(7,13] 0.000000e+00
## Datesenttocompany.juliandate 0.000000e+00
## Datesenttocompany.last32.log1p 0.000000e+00
## Datesenttocompany.month.fctr04 0.000000e+00
## Datesenttocompany.month.fctr10 0.000000e+00
## Datesenttocompany.month.fctr11 0.000000e+00
## Datesenttocompany.wkday.fctr1 0.000000e+00
## Datesenttocompany.wkday.fctr2 0.000000e+00
## Datesenttocompany.wkday.fctr3 0.000000e+00
## Datesenttocompany.wkday.fctr5 0.000000e+00
## Datesenttocompany.wkday.fctr6 0.000000e+00
## Datesenttocompany.year.fctr2016 0.000000e+00
## Response.fctrInprogress 0.000000e+00
## Response.fctrUntimelyresponse 0.000000e+00
## Rgn.Dvn.fctrMW#EastNorthCentral 0.000000e+00
## Rgn.Dvn.fctrOT#Other 0.000000e+00
## Rgn.Dvn.fctrSH#WestSouthCentral 0.000000e+00
## Sent.Recd.Dys.nexp 0.000000e+00
## gCConsent.fctrConsentnotprovided 0.000000e+00
## gCConsent.fctrConsentprovided 0.000000e+00
## gCompany.fctrCitibank 0.000000e+00
## gCompany.fctrComerica 0.000000e+00
## gCompany.fctrU.S.Bancorp 0.000000e+00
## gSPrd.fctr 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctr:.clusterid.fctr2 0.000000e+00
## gSPrd.fctr:.clusterid.fctr3 0.000000e+00
## gSPrd.fctr:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrAuto 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCheckcashing 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCheckingaccount 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM) 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr2 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr3 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr4 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCreditcard 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCreditrepair 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrDebtsettlement 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFHAmortgage 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFederalstudentloan 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGeneralpurposecard 0.000000e+00
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGiftormerchantcard 0.000000e+00
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrIDprepaidcard 0.000000e+00
## gSPrd.fctrIDprepaidcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrIDprepaidcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrIDprepaidcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrIdonotknow 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrInstallmentloan 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMedical 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMobilewallet:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMoneyorder 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMortgage 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrNon-federalstudentloan 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.) 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOther:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOtherbankproduct/service 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOthermortgage 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPawnloan 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPaydayloan 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPersonallineofcredit 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrRefundanticipationcheck 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrReversemortgage 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrSavingsaccount 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrSecondmortgage 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrTitleloan 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVAmortgage 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVehiclelease 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVehicleloan 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr4 0.000000e+00
## imp
## Channel.fctrFax 1.000000e+02
## Response.fctrClosedwithnon-monetaryrelief 9.048980e+01
## Response.fctrClosedwithmonetaryrelief 7.115750e+01
## gSPrd.fctrMobilewallet:.clusterid.fctr3 6.210649e+01
## gCompany.fctrNetSpendCorporation,aTSYSCompany 5.424758e+01
## Channel.fctrPhone 4.790375e+01
## gCompany.fctrAmex 4.394697e+01
## gCConsent.fctrOther 3.747417e+01
## gTags.fctrServicemember 3.475599e+01
## Datesenttocompany.month.fctr06 3.296016e+01
## Rgn.Dvn.fctrSH#EastSouthCentral 3.170696e+01
## gSPrd.fctrOther:.clusterid.fctr2 3.086994e+01
## Datesenttocompany.wkend 2.650603e+01
## gCompany.fctrBlackhawkNetworkHoldingsInc. 2.636093e+01
## Datesenttocompany.month.fctr05 2.499726e+01
## Channel.fctrPostalmail 2.460808e+01
## Datesenttocompany.month.fctr07 2.422671e+01
## gTags.fctrOlderAmerican 2.402958e+01
## Response.fctrClosed 2.396048e+01
## Datesenttocompany.month.fctr08 2.198540e+01
## Channel.fctrReferral 2.146433e+01
## Datesenttocompany.month.fctr12 1.956448e+01
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr2 1.847199e+01
## gSPrd.fctrMobilewallet 1.309092e+01
## Rgn.Dvn.fctrWT#Pacific 9.574300e+00
## Datesenttocompany.month.fctr03 9.444201e+00
## gSPrd.fctrOther:.clusterid.fctr4 8.711653e+00
## Rgn.Dvn.fctrMW#WestNorthCentral 7.339031e+00
## gCompany.fctrPayPalHoldings,Inc. 6.387562e+00
## gSPrd.fctrPayrollcard 6.151655e+00
## Rgn.Dvn.fctrWT#Mountain 5.576296e+00
## Datereceived.wkday.fctr4 4.792224e+00
## Datesenttocompany.last2.log1p 4.069618e+00
## Datesenttocompany.date.fctr(13,19] 3.855561e+00
## Sent.Recd.Dys.root2 3.127879e+00
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3 3.021032e+00
## gCompany.fctrOther 2.329106e+00
## Datesenttocompany.wkday.fctr4 2.078054e+00
## Rgn.Dvn.fctrMW#WestNorthCentral#NewEngland 1.864166e+00
## Datesenttocompany.year.fctr2015 1.448724e+00
## Datesenttocompany.last16.log1p 1.323848e+00
## gTags.fctrOlderAmericanServicemember 1.226441e+00
## Datesenttocompany.last4.log1p 9.932198e-01
## Datesenttocompany.last8.log1p 9.719498e-01
## gSPrd.fctrMobilewallet:.clusterid.fctr2 8.411298e-01
## Rgn.Dvn.fctrMW#WestNorthCentral#MidAtlantic 6.010509e-01
## Datesenttocompany.month.fctr09 5.752737e-01
## Sent.Recd.Dys 1.038013e-01
## Datesenttocompany.month.fctr02 2.781527e-02
## .pos 9.709951e-05
## .rnorm 0.000000e+00
## ComplaintID 0.000000e+00
## Datereceived.wkday.fctr1 0.000000e+00
## Datereceived.wkday.fctr2 0.000000e+00
## Datereceived.wkday.fctr3 0.000000e+00
## Datereceived.wkday.fctr5 0.000000e+00
## Datereceived.wkday.fctr6 0.000000e+00
## Datesenttocompany.date.fctr(19,25] 0.000000e+00
## Datesenttocompany.date.fctr(25,31] 0.000000e+00
## Datesenttocompany.date.fctr(7,13] 0.000000e+00
## Datesenttocompany.juliandate 0.000000e+00
## Datesenttocompany.last32.log1p 0.000000e+00
## Datesenttocompany.month.fctr04 0.000000e+00
## Datesenttocompany.month.fctr10 0.000000e+00
## Datesenttocompany.month.fctr11 0.000000e+00
## Datesenttocompany.wkday.fctr1 0.000000e+00
## Datesenttocompany.wkday.fctr2 0.000000e+00
## Datesenttocompany.wkday.fctr3 0.000000e+00
## Datesenttocompany.wkday.fctr5 0.000000e+00
## Datesenttocompany.wkday.fctr6 0.000000e+00
## Datesenttocompany.year.fctr2016 0.000000e+00
## Response.fctrInprogress 0.000000e+00
## Response.fctrUntimelyresponse 0.000000e+00
## Rgn.Dvn.fctrMW#EastNorthCentral 0.000000e+00
## Rgn.Dvn.fctrOT#Other 0.000000e+00
## Rgn.Dvn.fctrSH#WestSouthCentral 0.000000e+00
## Sent.Recd.Dys.nexp 0.000000e+00
## gCConsent.fctrConsentnotprovided 0.000000e+00
## gCConsent.fctrConsentprovided 0.000000e+00
## gCompany.fctrCitibank 0.000000e+00
## gCompany.fctrComerica 0.000000e+00
## gCompany.fctrU.S.Bancorp 0.000000e+00
## gSPrd.fctr 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctr(CD)Certificateofdeposit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctr:.clusterid.fctr2 0.000000e+00
## gSPrd.fctr:.clusterid.fctr3 0.000000e+00
## gSPrd.fctr:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrAuto 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrAuto:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCashingacheckwithoutanaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCheckcashing 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCheckcashing:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCheckingaccount 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCheckingaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM) 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr2 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr3 0.000000e+00
## gSPrd.fctrConventionaladjustablemortgage(ARM):.clusterid.fctr4 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrConventionalfixedmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCreditcard 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCreditcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrCreditrepair 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrCreditrepair:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrDebtsettlement 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrDebtsettlement:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrDomestic(US)moneytransfer:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFHAmortgage 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFHAmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFederalstudentloan 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFederalstudentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrFederalstudentloanservicing:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrForeigncurrencyexchange:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGeneralpurposecard 0.000000e+00
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGiftormerchantcard 0.000000e+00
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrGovernmentbenefitpaymentcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrHomeequityloanorlineofcredit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrIDprepaidcard 0.000000e+00
## gSPrd.fctrIDprepaidcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrIDprepaidcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrIDprepaidcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrIdonotknow 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrIdonotknow:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrInstallmentloan 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrInstallmentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrInternationalmoneytransfer:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMedical 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMedical:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMobilewallet:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMoneyorder 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMoneyorder:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrMortgage 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrMortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrNon-federalstudentloan 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrNon-federalstudentloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.) 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOther(i.e.phone,healthclub,etc.):.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOther:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOtherbankproduct/service 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOtherbankproduct/service:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrOthermortgage 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrOthermortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPawnloan 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPawnloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPaydayloan 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPaydayloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPayrollcard:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrPersonallineofcredit 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrPersonallineofcredit:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrRefundanticipationcheck 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrRefundanticipationcheck:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrReversemortgage 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrReversemortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrSavingsaccount 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrSavingsaccount:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrSecondmortgage 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrSecondmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrTitleloan 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrTitleloan:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrTraveler\342\200\231s/Cashier\342\200\231schecks:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVAmortgage 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVAmortgage:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVehiclelease 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVehiclelease:.clusterid.fctr4 0.000000e+00
## gSPrd.fctrVehicleloan 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr2 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr3 0.000000e+00
## gSPrd.fctrVehicleloan:.clusterid.fctr4 0.000000e+00if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFnlNslId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSltId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFnlNslId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFnlNslId, : Limiting important feature scatter plots to 5 out of 26
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## ComplaintID CDisputed.fctr CDisputed.fctr.Low.cor.X..rcv.glmnet.prob
## 1 1617632 Y 0.02380613
## 2 1623265 Y NA
## 3 1623363 Y 0.02774190
## 4 1604897 Y 0.03669799
## 5 1629220 Y 0.03549904
## 6 1617605 Y 0.03623692
## CDisputed.fctr.Low.cor.X..rcv.glmnet
## 1 N
## 2 <NA>
## 3 N
## 4 N
## 5 N
## 6 N
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err
## 1 TRUE
## 2 NA
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err.abs
## 1 0.9761939
## 2 NA
## 3 0.9722581
## 4 0.9633020
## 5 0.9645010
## 6 0.9637631
## CDisputed.fctr.Low.cor.X..rcv.glmnet.is.acc
## 1 FALSE
## 2 NA
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.prob
## 1 0.02132845
## 2 0.02549284
## 3 0.02973816
## 4 0.03009988
## 5 0.03303351
## 6 0.03428325
## CDisputed.fctr.Trn.Low.cor.X...glmnet
## 1 N
## 2 N
## 3 N
## 4 N
## 5 N
## 6 N
## CDisputed.fctr.Trn.Low.cor.X...glmnet.err
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.err.abs
## 1 0.9786715
## 2 0.9745072
## 3 0.9702618
## 4 0.9699001
## 5 0.9669665
## 6 0.9657168
## CDisputed.fctr.Trn.Low.cor.X...glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.error
## 1 -0.4786715
## 2 -0.4745072
## 3 -0.4702618
## 4 -0.4699001
## 5 -0.4669665
## 6 -0.4657168
## ComplaintID CDisputed.fctr CDisputed.fctr.Low.cor.X..rcv.glmnet.prob
## 209 1388650 Y 0.2455317
## 218 1339659 Y 0.2132799
## 235 968052 Y NA
## 325 1064690 Y 0.3379581
## 362 1792850 Y 0.4924318
## 400 2100713 Y 0.4804539
## CDisputed.fctr.Low.cor.X..rcv.glmnet
## 209 N
## 218 N
## 235 <NA>
## 325 N
## 362 N
## 400 N
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err
## 209 TRUE
## 218 TRUE
## 235 NA
## 325 TRUE
## 362 TRUE
## 400 TRUE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err.abs
## 209 0.7544683
## 218 0.7867201
## 235 NA
## 325 0.6620419
## 362 0.5075682
## 400 0.5195461
## CDisputed.fctr.Low.cor.X..rcv.glmnet.is.acc
## 209 FALSE
## 218 FALSE
## 235 NA
## 325 FALSE
## 362 FALSE
## 400 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.prob
## 209 0.2300465
## 218 0.2332135
## 235 0.2453523
## 325 0.3174201
## 362 0.3588949
## 400 0.4251243
## CDisputed.fctr.Trn.Low.cor.X...glmnet
## 209 N
## 218 N
## 235 N
## 325 N
## 362 N
## 400 N
## CDisputed.fctr.Trn.Low.cor.X...glmnet.err
## 209 TRUE
## 218 TRUE
## 235 TRUE
## 325 TRUE
## 362 TRUE
## 400 TRUE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.err.abs
## 209 0.7699535
## 218 0.7667865
## 235 0.7546477
## 325 0.6825799
## 362 0.6411051
## 400 0.5748757
## CDisputed.fctr.Trn.Low.cor.X...glmnet.is.acc
## 209 FALSE
## 218 FALSE
## 235 FALSE
## 325 FALSE
## 362 FALSE
## 400 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.accurate
## 209 FALSE
## 218 FALSE
## 235 FALSE
## 325 FALSE
## 362 FALSE
## 400 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.error
## 209 -0.26995346
## 218 -0.26678652
## 235 -0.25464767
## 325 -0.18257991
## 362 -0.14110515
## 400 -0.07487574
## ComplaintID CDisputed.fctr CDisputed.fctr.Low.cor.X..rcv.glmnet.prob
## 424 1996873 N 0.5507196
## 425 1275358 N NA
## 426 1911555 N 0.5360307
## 427 1483131 N NA
## 428 2007045 N 0.5984713
## 429 2022079 N 0.5743428
## CDisputed.fctr.Low.cor.X..rcv.glmnet
## 424 Y
## 425 <NA>
## 426 Y
## 427 <NA>
## 428 Y
## 429 Y
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err
## 424 TRUE
## 425 NA
## 426 TRUE
## 427 NA
## 428 TRUE
## 429 TRUE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err.abs
## 424 0.5507196
## 425 NA
## 426 0.5360307
## 427 NA
## 428 0.5984713
## 429 0.5743428
## CDisputed.fctr.Low.cor.X..rcv.glmnet.is.acc
## 424 FALSE
## 425 NA
## 426 FALSE
## 427 NA
## 428 FALSE
## 429 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.prob
## 424 0.5110777
## 425 0.5111699
## 426 0.5121616
## 427 0.5410975
## 428 0.5424910
## 429 0.5432355
## CDisputed.fctr.Trn.Low.cor.X...glmnet
## 424 Y
## 425 Y
## 426 Y
## 427 Y
## 428 Y
## 429 Y
## CDisputed.fctr.Trn.Low.cor.X...glmnet.err
## 424 TRUE
## 425 TRUE
## 426 TRUE
## 427 TRUE
## 428 TRUE
## 429 TRUE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.err.abs
## 424 0.5110777
## 425 0.5111699
## 426 0.5121616
## 427 0.5410975
## 428 0.5424910
## 429 0.5432355
## CDisputed.fctr.Trn.Low.cor.X...glmnet.is.acc
## 424 FALSE
## 425 FALSE
## 426 FALSE
## 427 FALSE
## 428 FALSE
## 429 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.accurate
## 424 FALSE
## 425 FALSE
## 426 FALSE
## 427 FALSE
## 428 FALSE
## 429 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.error
## 424 0.01107770
## 425 0.01116991
## 426 0.01216161
## 427 0.04109755
## 428 0.04249104
## 429 0.04323555## Warning in glb_get_predictions(df = tileDf, mdl$.myId, rsp_var,
## prob_threshold): Using default probability threshold: 0.5## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## ComplaintID CDisputed.fctr CDisputed.fctr.Low.cor.X..rcv.glmnet.prob
## 1 1617632 Y 0.02380613
## 2 1623265 Y NA
## 3 1623363 Y 0.02774190
## 4 1604897 Y 0.03669799
## 5 1629220 Y 0.03549904
## 6 1617605 Y 0.03623692
## CDisputed.fctr.Low.cor.X..rcv.glmnet
## 1 N
## 2 <NA>
## 3 N
## 4 N
## 5 N
## 6 N
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err
## 1 TRUE
## 2 NA
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err.abs
## 1 0.9761939
## 2 NA
## 3 0.9722581
## 4 0.9633020
## 5 0.9645010
## 6 0.9637631
## CDisputed.fctr.Low.cor.X..rcv.glmnet.is.acc
## 1 FALSE
## 2 NA
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.prob
## 1 0.02132845
## 2 0.02549284
## 3 0.02973816
## 4 0.03009988
## 5 0.03303351
## 6 0.03428325
## CDisputed.fctr.Trn.Low.cor.X...glmnet
## 1 N
## 2 N
## 3 N
## 4 N
## 5 N
## 6 N
## CDisputed.fctr.Trn.Low.cor.X...glmnet.err
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.err.abs
## 1 0.9786715
## 2 0.9745072
## 3 0.9702618
## 4 0.9699001
## 5 0.9669665
## 6 0.9657168
## CDisputed.fctr.Trn.Low.cor.X...glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.error
## 1 -0.4786715
## 2 -0.4745072
## 3 -0.4702618
## 4 -0.4699001
## 5 -0.4669665
## 6 -0.4657168
## ComplaintID CDisputed.fctr CDisputed.fctr.Low.cor.X..rcv.glmnet.prob
## 20 1129509 Y 0.05294662
## 41 1634714 Y NA
## 63 1345649 Y 0.08135015
## 107 1690141 Y 0.14391037
## 243 1711162 Y NA
## 300 1715455 Y 0.32862763
## CDisputed.fctr.Low.cor.X..rcv.glmnet
## 20 N
## 41 <NA>
## 63 N
## 107 N
## 243 <NA>
## 300 N
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err
## 20 TRUE
## 41 NA
## 63 TRUE
## 107 TRUE
## 243 NA
## 300 TRUE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err.abs
## 20 0.9470534
## 41 NA
## 63 0.9186498
## 107 0.8560896
## 243 NA
## 300 0.6713724
## CDisputed.fctr.Low.cor.X..rcv.glmnet.is.acc
## 20 FALSE
## 41 NA
## 63 FALSE
## 107 FALSE
## 243 NA
## 300 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.prob
## 20 0.06040075
## 41 0.09195872
## 63 0.11262217
## 107 0.15606941
## 243 0.25244735
## 300 0.29889718
## CDisputed.fctr.Trn.Low.cor.X...glmnet
## 20 N
## 41 N
## 63 N
## 107 N
## 243 N
## 300 N
## CDisputed.fctr.Trn.Low.cor.X...glmnet.err
## 20 TRUE
## 41 TRUE
## 63 TRUE
## 107 TRUE
## 243 TRUE
## 300 TRUE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.err.abs
## 20 0.9395993
## 41 0.9080413
## 63 0.8873778
## 107 0.8439306
## 243 0.7475527
## 300 0.7011028
## CDisputed.fctr.Trn.Low.cor.X...glmnet.is.acc
## 20 FALSE
## 41 FALSE
## 63 FALSE
## 107 FALSE
## 243 FALSE
## 300 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.accurate
## 20 FALSE
## 41 FALSE
## 63 FALSE
## 107 FALSE
## 243 FALSE
## 300 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.error
## 20 -0.4395993
## 41 -0.4080413
## 63 -0.3873778
## 107 -0.3439306
## 243 -0.2475527
## 300 -0.2011028
## ComplaintID CDisputed.fctr CDisputed.fctr.Low.cor.X..rcv.glmnet.prob
## 424 1996873 N 0.5507196
## 425 1275358 N NA
## 426 1911555 N 0.5360307
## 427 1483131 N NA
## 428 2007045 N 0.5984713
## 429 2022079 N 0.5743428
## CDisputed.fctr.Low.cor.X..rcv.glmnet
## 424 Y
## 425 <NA>
## 426 Y
## 427 <NA>
## 428 Y
## 429 Y
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err
## 424 TRUE
## 425 NA
## 426 TRUE
## 427 NA
## 428 TRUE
## 429 TRUE
## CDisputed.fctr.Low.cor.X..rcv.glmnet.err.abs
## 424 0.5507196
## 425 NA
## 426 0.5360307
## 427 NA
## 428 0.5984713
## 429 0.5743428
## CDisputed.fctr.Low.cor.X..rcv.glmnet.is.acc
## 424 FALSE
## 425 NA
## 426 FALSE
## 427 NA
## 428 FALSE
## 429 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.prob
## 424 0.5110777
## 425 0.5111699
## 426 0.5121616
## 427 0.5410975
## 428 0.5424910
## 429 0.5432355
## CDisputed.fctr.Trn.Low.cor.X...glmnet
## 424 Y
## 425 Y
## 426 Y
## 427 Y
## 428 Y
## 429 Y
## CDisputed.fctr.Trn.Low.cor.X...glmnet.err
## 424 TRUE
## 425 TRUE
## 426 TRUE
## 427 TRUE
## 428 TRUE
## 429 TRUE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.err.abs
## 424 0.5110777
## 425 0.5111699
## 426 0.5121616
## 427 0.5410975
## 428 0.5424910
## 429 0.5432355
## CDisputed.fctr.Trn.Low.cor.X...glmnet.is.acc
## 424 FALSE
## 425 FALSE
## 426 FALSE
## 427 FALSE
## 428 FALSE
## 429 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.accurate
## 424 FALSE
## 425 FALSE
## 426 FALSE
## 427 FALSE
## 428 FALSE
## 429 FALSE
## CDisputed.fctr.Trn.Low.cor.X...glmnet.error
## 424 0.01107770
## 425 0.01116991
## 426 0.01216161
## 427 0.04109755
## 428 0.04249104
## 429 0.04323555## Warning in glb_get_predictions(df = tileDf, mdl$.myId, rsp_var,
## prob_threshold): Using default probability threshold: 0.5## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "CDisputed.fctr.Trn.Low.cor.X...glmnet.prob"
## [2] "CDisputed.fctr.Trn.Low.cor.X...glmnet"
## [3] "CDisputed.fctr.Trn.Low.cor.X...glmnet.err"
## [4] "CDisputed.fctr.Trn.Low.cor.X...glmnet.err.abs"
## [5] "CDisputed.fctr.Trn.Low.cor.X...glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.training.all.prediction
## 2.0000 5 2 0 0 1## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: model.final
## 3.0000 4 2 0 1 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 7 fit.data.training 3 1 1 183.426 189.843
## 8 predict.data.new 4 0 0 189.843 NA
## elapsed
## 7 6.417
## 8 NAStep 4.0: predict data new
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFnlNslId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.5## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFnlRslId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.5## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFnlNslId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.5## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFnlRslId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.5## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFnlNslId, : Limiting important feature scatter plots to 5 out of 26
## NULL
## NULL## Loading required package: tidyr##
## Attaching package: 'tidyr'## The following object is masked from 'package:Matrix':
##
## expand## The following object is masked from 'package:reshape2':
##
## smiths## [1] "OOBobs CDisputed.fctr.Low.cor.X..rcv.glmnet N: min < min of Train range: 1"
## ComplaintID CDisputed.fctr.Low.cor.X..rcv.glmnet ComplaintID.1
## 449250 944774 N 944774
## id cor.y exclude.as.feat cor.y.abs
## ComplaintID ComplaintID 0.02064725 FALSE 0.02064725
## cor.high.X freqRatio percentUnique zeroVar nzv
## ComplaintID Datereceived.year.fctr 1 100 FALSE FALSE
## is.cor.y.abs.low interaction.feat shapiro.test.p.value
## ComplaintID FALSE <NA> 1.176977e-27
## rsp_var_raw id_var rsp_var max min max.CDisputed.fctr.N
## ComplaintID FALSE TRUE NA 2148649 944774 2053951
## max.CDisputed.fctr.Y min.CDisputed.fctr.N min.CDisputed.fctr.Y
## ComplaintID 2135188 945559 948171
## max.CDisputed.fctr.Low.cor.X..rcv.glmnet.N
## ComplaintID 2090410
## max.CDisputed.fctr.Low.cor.X..rcv.glmnet.Y
## ComplaintID 2035556
## min.CDisputed.fctr.Low.cor.X..rcv.glmnet.N
## ComplaintID 944774
## min.CDisputed.fctr.Low.cor.X..rcv.glmnet.Y
## ComplaintID 1247838
## max.CDisputed.fctr.Trn.Low.cor.X...glmnet.N
## ComplaintID 2148649
## max.CDisputed.fctr.Trn.Low.cor.X...glmnet.Y
## ComplaintID 2087640
## min.CDisputed.fctr.Trn.Low.cor.X...glmnet.N
## ComplaintID 1143139
## min.CDisputed.fctr.Trn.Low.cor.X...glmnet.Y
## ComplaintID 2073896
## [1] "OOBobs CDisputed.fctr.Low.cor.X..rcv.glmnet N: max > max of Train range: 4"
## ComplaintID CDisputed.fctr.Low.cor.X..rcv.glmnet .pos
## 603242 2075942 N 603242
## 582578 2078429 N 582578
## 594096 2090410 N 594096
## 606975 2057714 N 606975
## ComplaintID.1
## 603242 2075942
## 582578 2078429
## 594096 2090410
## 606975 2057714
## id cor.y exclude.as.feat cor.y.abs
## .pos .pos 0.06908565 FALSE 0.06908565
## ComplaintID ComplaintID 0.02064725 FALSE 0.02064725
## cor.high.X freqRatio percentUnique zeroVar nzv
## .pos <NA> 1 100 FALSE FALSE
## ComplaintID Datereceived.year.fctr 1 100 FALSE FALSE
## is.cor.y.abs.low interaction.feat shapiro.test.p.value
## .pos FALSE <NA> 7.063292e-35
## ComplaintID FALSE <NA> 1.176977e-27
## rsp_var_raw id_var rsp_var max min max.CDisputed.fctr.N
## .pos FALSE NA NA 646730 31668 606875
## ComplaintID FALSE TRUE NA 2148649 944774 2053951
## max.CDisputed.fctr.Y min.CDisputed.fctr.N min.CDisputed.fctr.Y
## .pos 607184 31668 31802
## ComplaintID 2135188 945559 948171
## max.CDisputed.fctr.Low.cor.X..rcv.glmnet.N
## .pos 606975
## ComplaintID 2090410
## max.CDisputed.fctr.Low.cor.X..rcv.glmnet.Y
## .pos 600256
## ComplaintID 2035556
## min.CDisputed.fctr.Low.cor.X..rcv.glmnet.N
## .pos 31800
## ComplaintID 944774
## min.CDisputed.fctr.Low.cor.X..rcv.glmnet.Y
## .pos 331117
## ComplaintID 1247838
## max.CDisputed.fctr.Trn.Low.cor.X...glmnet.N
## .pos 646730
## ComplaintID 2148649
## max.CDisputed.fctr.Trn.Low.cor.X...glmnet.Y
## .pos 625189
## ComplaintID 2087640
## min.CDisputed.fctr.Trn.Low.cor.X...glmnet.N
## .pos 608363
## ComplaintID 1143139
## min.CDisputed.fctr.Trn.Low.cor.X...glmnet.Y
## .pos 621368
## ComplaintID 2073896
## [1] "OOBobs total range outliers: 5"
## [1] "newobs CDisputed.fctr.Trn.Low.cor.X...glmnet N: max > max of Train range: 163"
## ComplaintID CDisputed.fctr.Trn.Low.cor.X...glmnet .pos
## 608363 1143139 N 608363
## 608720 1828373 N 608720
## 610146 1145704 N 610146
## 614778 2051177 N 614778
## 615095 2067173 N 615095
## 615253 2048191 N 615253
## ComplaintID.1
## 608363 1143139
## 608720 1828373
## 610146 1145704
## 614778 2051177
## 615095 2067173
## 615253 2048191
## ComplaintID CDisputed.fctr.Trn.Low.cor.X...glmnet .pos
## 616364 2033319 N 616364
## 616964 2045346 N 616964
## 621719 2081001 N 621719
## 626314 2088307 N 626314
## 629023 2104534 N 629023
## 646215 2140626 N 646215
## ComplaintID.1
## 616364 2033319
## 616964 2045346
## 621719 2081001
## 626314 2088307
## 629023 2104534
## 646215 2140626
## ComplaintID CDisputed.fctr.Trn.Low.cor.X...glmnet .pos
## 645900 2127603 N 645900
## 646016 2148649 N 646016
## 646215 2140626 N 646215
## 646216 2144255 N 646216
## 646392 2141316 N 646392
## 646730 2094452 N 646730
## ComplaintID.1
## 645900 2127603
## 646016 2148649
## 646215 2140626
## 646216 2144255
## 646392 2141316
## 646730 2094452
## id cor.y exclude.as.feat cor.y.abs
## .pos .pos 0.06908565 FALSE 0.06908565
## ComplaintID ComplaintID 0.02064725 FALSE 0.02064725
## cor.high.X freqRatio percentUnique zeroVar nzv
## .pos <NA> 1 100 FALSE FALSE
## ComplaintID Datereceived.year.fctr 1 100 FALSE FALSE
## is.cor.y.abs.low interaction.feat shapiro.test.p.value
## .pos FALSE <NA> 7.063292e-35
## ComplaintID FALSE <NA> 1.176977e-27
## rsp_var_raw id_var rsp_var max min max.CDisputed.fctr.N
## .pos FALSE NA NA 646730 31668 606875
## ComplaintID FALSE TRUE NA 2148649 944774 2053951
## max.CDisputed.fctr.Y min.CDisputed.fctr.N min.CDisputed.fctr.Y
## .pos 607184 31668 31802
## ComplaintID 2135188 944774 948171
## max.CDisputed.fctr.Low.cor.X..rcv.glmnet.N
## .pos 606975
## ComplaintID 2090410
## max.CDisputed.fctr.Low.cor.X..rcv.glmnet.Y
## .pos 600256
## ComplaintID 2035556
## min.CDisputed.fctr.Low.cor.X..rcv.glmnet.N
## .pos 31800
## ComplaintID 944774
## min.CDisputed.fctr.Low.cor.X..rcv.glmnet.Y
## .pos 331117
## ComplaintID 1247838
## max.CDisputed.fctr.Trn.Low.cor.X...glmnet.N
## .pos 646730
## ComplaintID 2148649
## max.CDisputed.fctr.Trn.Low.cor.X...glmnet.Y
## .pos 625189
## ComplaintID 2087640
## min.CDisputed.fctr.Trn.Low.cor.X...glmnet.N
## .pos 608363
## ComplaintID 1143139
## min.CDisputed.fctr.Trn.Low.cor.X...glmnet.Y
## .pos 621368
## ComplaintID 2073896
## [1] "newobs CDisputed.fctr.Trn.Low.cor.X...glmnet Y: max > max of Train range: 2"
## ComplaintID CDisputed.fctr.Trn.Low.cor.X...glmnet .pos
## 621368 2073896 Y 621368
## 625189 2087640 Y 625189
## id cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## .pos .pos 0.06908565 FALSE 0.06908565 <NA> 1
## percentUnique zeroVar nzv is.cor.y.abs.low interaction.feat
## .pos 100 FALSE FALSE FALSE <NA>
## shapiro.test.p.value rsp_var_raw id_var rsp_var max min
## .pos 7.063292e-35 FALSE NA NA 646730 31668
## max.CDisputed.fctr.N max.CDisputed.fctr.Y min.CDisputed.fctr.N
## .pos 606875 607184 31668
## min.CDisputed.fctr.Y max.CDisputed.fctr.Low.cor.X..rcv.glmnet.N
## .pos 31802 606975
## max.CDisputed.fctr.Low.cor.X..rcv.glmnet.Y
## .pos 600256
## min.CDisputed.fctr.Low.cor.X..rcv.glmnet.N
## .pos 31800
## min.CDisputed.fctr.Low.cor.X..rcv.glmnet.Y
## .pos 331117
## max.CDisputed.fctr.Trn.Low.cor.X...glmnet.N
## .pos 646730
## max.CDisputed.fctr.Trn.Low.cor.X...glmnet.Y
## .pos 625189
## min.CDisputed.fctr.Trn.Low.cor.X...glmnet.N
## .pos 608363
## min.CDisputed.fctr.Trn.Low.cor.X...glmnet.Y
## .pos 621368
## [1] "newobs total range outliers: 165"## [1] 0.5## [1] "glbMdlSltId: Low.cor.X##rcv#glmnet"## [1] "glbMdlFnlNslId: Trn.Low.cor.X###glmnet"## [1] "Cross Validation issues:"## Warning in glbgetDisplayModelsDf(): Cross Validation issues:## MFO###myMFO_classfr Random###myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y##rcv#rpart
## 0 1
## Trn.Low.cor.X###glmnet
## 0## max.Accuracy.OOB max.AUCROCR.OOB
## Low.cor.X##rcv#glmnet 0.8539130 0.7136134
## All.X##rcv#glmnet 0.8539130 0.7134214
## All.X##rcv#glm 0.8521739 0.7127491
## Max.cor.Y.Time.Lag##rcv#glmnet 0.8521739 0.6766867
## Max.cor.Y.rcv.1X1###glmnet 0.8521739 0.6620888
## MFO###myMFO_classfr 0.8521739 0.5000000
## Max.cor.Y##rcv#rpart 0.8521739 0.5000000
## Random###myrandom_classfr 0.8521739 0.4994598
## Trn.Low.cor.X###glmnet NA NA
## Trn.Low.cor.X##rcv#glmnet NA NA
## max.AUCpROC.OOB min.elapsedtime.everything
## Low.cor.X##rcv#glmnet 0.5156062 13.573
## All.X##rcv#glmnet 0.5145858 18.452
## All.X##rcv#glm 0.5179472 47.994
## Max.cor.Y.Time.Lag##rcv#glmnet 0.5000000 2.615
## Max.cor.Y.rcv.1X1###glmnet 0.5000000 0.545
## MFO###myMFO_classfr 0.5000000 0.209
## Max.cor.Y##rcv#rpart 0.5000000 1.518
## Random###myrandom_classfr 0.4971188 0.186
## Trn.Low.cor.X###glmnet NA 0.902
## Trn.Low.cor.X##rcv#glmnet NA 14.451
## max.Accuracy.fit opt.prob.threshold.fit
## Low.cor.X##rcv#glmnet 0.8484475 0.40
## All.X##rcv#glmnet 0.8481571 0.40
## All.X##rcv#glm 0.8453978 0.45
## Max.cor.Y.Time.Lag##rcv#glmnet 0.8465570 0.50
## Max.cor.Y.rcv.1X1###glmnet 0.8465562 0.50
## MFO###myMFO_classfr 0.8465562 0.50
## Max.cor.Y##rcv#rpart 0.8465570 0.50
## Random###myrandom_classfr 0.8465562 0.85
## Trn.Low.cor.X###glmnet 0.8539561 0.40
## Trn.Low.cor.X##rcv#glmnet 0.8488446 0.40
## opt.prob.threshold.OOB
## Low.cor.X##rcv#glmnet 0.50
## All.X##rcv#glmnet 0.60
## All.X##rcv#glm 0.65
## Max.cor.Y.Time.Lag##rcv#glmnet 0.50
## Max.cor.Y.rcv.1X1###glmnet 0.50
## MFO###myMFO_classfr 0.50
## Max.cor.Y##rcv#rpart 0.50
## Random###myrandom_classfr 0.85
## Trn.Low.cor.X###glmnet NA
## Trn.Low.cor.X##rcv#glmnet NA## [1] "Low.cor.X##rcv#glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 488 2
## Y 82 3## err.abs.fit.sum err.abs.OOB.sum
## Giftormerchantcard 58.96237 21.714241
## Mobilewallet 72.91780 17.462991
## Other 38.57401 7.921361
## IDprepaidcard 29.97656 6.896103
## Governmentbenefitpaymentcard 40.79012 17.945749
## Generalpurposecard 246.61968 56.641561
## Payrollcard 34.73402 8.086594
## err.abs.trn.sum err.abs.new.sum
## Giftormerchantcard 78.76515 NA
## Mobilewallet 89.09415 NA
## Other 46.01171 NA
## IDprepaidcard 36.76035 NA
## Governmentbenefitpaymentcard 58.77876 NA
## Generalpurposecard 305.34109 NA
## Payrollcard 43.78600 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst
## Giftormerchantcard 0.08718396 0.12869565 0.12727273
## Mobilewallet 0.10549259 0.11130435 0.10909091
## Other 0.06495205 0.05565217 0.05454545
## IDprepaidcard 0.05972101 0.05043478 0.04848485
## Governmentbenefitpaymentcard 0.08064516 0.13217391 0.13333333
## Generalpurposecard 0.47951177 0.41913043 0.41818182
## Payrollcard 0.12249346 0.10260870 0.10909091
## .n.Fit .n.New.N .n.New.Y .n.OOB .n.Trn.N
## Giftormerchantcard 200 20 1 74 222
## Mobilewallet 242 18 NA 64 240
## Other 149 9 NA 32 150
## IDprepaidcard 137 8 NA 29 142
## Governmentbenefitpaymentcard 185 22 NA 76 224
## Generalpurposecard 1100 68 1 241 1138
## Payrollcard 281 18 NA 59 316
## .n.Trn.Y .n.Tst .n.fit .n.new .n.trn
## Giftormerchantcard 52 21 200 21 274
## Mobilewallet 66 18 242 18 306
## Other 31 9 149 9 181
## IDprepaidcard 24 8 137 8 166
## Governmentbenefitpaymentcard 37 22 185 22 261
## Generalpurposecard 203 69 1100 69 1341
## Payrollcard 24 18 281 18 340
## err.abs.OOB.mean err.abs.fit.mean
## Giftormerchantcard 0.2934357 0.2948118
## Mobilewallet 0.2728592 0.3013132
## Other 0.2475425 0.2588859
## IDprepaidcard 0.2377967 0.2188070
## Governmentbenefitpaymentcard 0.2361283 0.2204871
## Generalpurposecard 0.2350272 0.2241997
## Payrollcard 0.1370609 0.1236086
## err.abs.new.mean err.abs.trn.mean
## Giftormerchantcard NA 0.2874640
## Mobilewallet NA 0.2911574
## Other NA 0.2542083
## IDprepaidcard NA 0.2214479
## Governmentbenefitpaymentcard NA 0.2252060
## Generalpurposecard NA 0.2276966
## Payrollcard NA 0.1287823
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 522.574552 136.668600 658.537195 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.000000 1.000000 1.000000 2294.000000
## .n.New.N .n.New.Y .n.OOB .n.Trn.N
## 163.000000 NA 575.000000 2432.000000
## .n.Trn.Y .n.Tst .n.fit .n.new
## 437.000000 165.000000 2294.000000 165.000000
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 2869.000000 1.659851 1.642113 NA
## err.abs.trn.mean
## 1.635962## [1] "Features Importance for selected models:"## Low.cor.X..rcv.glmnet.imp
## Channel.fctrFax 100.000000
## Response.fctrClosedwithnon-monetaryrelief 83.293100
## Response.fctrClosedwithmonetaryrelief 78.857850
## gSPrd.fctrMobilewallet:.clusterid.fctr3 69.970179
## Datesenttocompany.month.fctr06 54.959539
## gCompany.fctrAmex 52.843646
## Channel.fctrPhone 45.981732
## gCompany.fctrNetSpendCorporation,aTSYSCompany 45.635189
## gTags.fctrServicemember 41.964230
## gSPrd.fctrOther:.clusterid.fctr2 37.222030
## Channel.fctrReferral 36.747721
## gCompany.fctrBlackhawkNetworkHoldingsInc. 28.167432
## Datesenttocompany.wkend 27.207536
## gCConsent.fctrOther 24.426738
## gTags.fctrOlderAmerican 23.153531
## gSPrd.fctrMobilewallet 20.069309
## Datesenttocompany.month.fctr05 19.859871
## Datesenttocompany.month.fctr11 19.439917
## Rgn.Dvn.fctrMW#EastNorthCentral 18.557544
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3 18.138503
## Datesenttocompany.month.fctr03 17.240396
## Datesenttocompany.month.fctr12 17.069223
## Datesenttocompany.month.fctr07 16.746237
## Response.fctrClosed 16.428546
## Rgn.Dvn.fctrMW#WestNorthCentral 15.376729
## Rgn.Dvn.fctrSH#EastSouthCentral 15.031025
## Rgn.Dvn.fctrWT#Mountain 12.552754
## gSPrd.fctrPayrollcard 11.480851
## Channel.fctrPostalmail 3.425176
## Datesenttocompany.month.fctr08 0.000000
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr2 0.000000
## Trn.Low.cor.X...glmnet.imp
## Channel.fctrFax 100.000000
## Response.fctrClosedwithnon-monetaryrelief 90.489796
## Response.fctrClosedwithmonetaryrelief 71.157499
## gSPrd.fctrMobilewallet:.clusterid.fctr3 62.106491
## Datesenttocompany.month.fctr06 32.960157
## gCompany.fctrAmex 43.946967
## Channel.fctrPhone 47.903749
## gCompany.fctrNetSpendCorporation,aTSYSCompany 54.247576
## gTags.fctrServicemember 34.755987
## gSPrd.fctrOther:.clusterid.fctr2 30.869941
## Channel.fctrReferral 21.464326
## gCompany.fctrBlackhawkNetworkHoldingsInc. 26.360930
## Datesenttocompany.wkend 26.506031
## gCConsent.fctrOther 37.474170
## gTags.fctrOlderAmerican 24.029585
## gSPrd.fctrMobilewallet 13.090919
## Datesenttocompany.month.fctr05 24.997264
## Datesenttocompany.month.fctr11 0.000000
## Rgn.Dvn.fctrMW#EastNorthCentral 0.000000
## gSPrd.fctrGeneralpurposecard:.clusterid.fctr3 3.021032
## Datesenttocompany.month.fctr03 9.444201
## Datesenttocompany.month.fctr12 19.564480
## Datesenttocompany.month.fctr07 24.226708
## Response.fctrClosed 23.960483
## Rgn.Dvn.fctrMW#WestNorthCentral 7.339031
## Rgn.Dvn.fctrSH#EastSouthCentral 31.706963
## Rgn.Dvn.fctrWT#Mountain 5.576296
## gSPrd.fctrPayrollcard 6.151655
## Channel.fctrPostalmail 24.608078
## Datesenttocompany.month.fctr08 21.985399
## gSPrd.fctrGiftormerchantcard:.clusterid.fctr2 18.471988## [1] "glbObsNew prediction stats:"##
## N Y
## 163 2## label step_major step_minor label_minor bgn end
## 8 predict.data.new 4 0 0 189.843 198.18
## 9 display.session.info 5 0 0 198.180 NA
## elapsed
## 8 8.337
## 9 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn end
## 3 fit.models 2 1 1 63.628 149.418
## 2 fit.models 2 0 0 7.982 63.627
## 6 fit.data.training 3 0 0 157.949 183.425
## 8 predict.data.new 4 0 0 189.843 198.180
## 4 fit.models 2 2 2 149.418 156.028
## 7 fit.data.training 3 1 1 183.426 189.843
## 1 select.features 1 0 0 4.371 7.982
## 5 fit.models 2 3 3 156.029 157.948
## elapsed duration
## 3 85.790 85.790
## 2 55.645 55.645
## 6 25.476 25.476
## 8 8.337 8.337
## 4 6.610 6.610
## 7 6.417 6.417
## 1 3.611 3.611
## 5 1.919 1.919
## [1] "Total Elapsed Time: 198.18 secs"
## label step_major step_minor label_minor bgn end
## 4 fit.models_1_All.X 1 3 glm 92.724 149.016
## 3 fit.models_1_All.X 1 2 glmnet 66.516 92.723
## 5 fit.models_1_preProc 1 4 preProc 149.017 149.412
## 1 fit.models_1_bgn 1 0 setup 66.502 66.511
## 2 fit.models_1_All.X 1 1 setup 66.511 66.516
## elapsed duration
## 4 56.292 56.292
## 3 26.207 26.207
## 5 0.395 0.395
## 1 0.009 0.009
## 2 0.005 0.005
## [1] "Total Elapsed Time: 149.412 secs"